essay about social discrimination

Essays About Discrimination: Top 5 Examples and 8 Prompts

You must know how to connect with your readers to write essays about discrimination effectively; read on for our top essay examples, including prompts that will help you write.

Discrimination comes in many forms and still happens to many individuals or groups today. It occurs when there’s a distinction or bias against someone because of their age, race, religion, gender, sexual orientation, or disability.

Discrimination can happen to anyone wherever and whenever they are. Unfortunately, it’s a problem that society is yet to solve entirely. Here are five in-depth examples of this theme’s subcategories to guide you in creating your essays about discrimination.

1. Essay On Discrimination For Students In Easy Words by Prateek

2. personal discrimination experience by naomi nakatani, 3. prejudice and discrimination by william anderson, 4. socioeconomic class discrimination in luca by krystal ibarra, 5. the new way of discrimination by writer bill, 1. my discrimination experience, 2. what can i do to stop discrimination, 3. discrimination in my community, 4. the cost of discrimination, 5. examples of discrimination, 6. discrimination in sports: segregating men and women, 7. how to stop my discrimination against others, 8. what should groups do to fight discrimination.

“In the current education system, the condition of education and its promotion of equality is very important. The education system should be a good place for each and every student. It must be on the basis of equal opportunities for each student in every country. It must be free of discrimination.”

Prateek starts his essay by telling the story of a student having difficulty getting admitted to a college because of high fees. He then poses the question of how the student will be able to get an education when he can’t have the opportunity to do so in the first place. He goes on to discuss UNESCO’s objectives against discrimination. 

Further in the essay, the author defines discrimination and cites instances when it happens. Prateek also compares past and present discrimination, ending the piece by saying it should stop and everyone deserves to be treated fairly.

“I thought that there is no discrimination before I actually had discrimination… I think we must treat everyone equally even though people speak different languages or have different colors of skin.”

In her short essay, Nakatani shares the experiences that made her feel discriminated against when she visited the US. She includes a fellow guest saying she and her mother can’t use the shared pool in a hotel they stay in because they are Japanese and getting cheated of her money when she bought from a small shop because she can’t speak English very well.

“Whether intentional or not, prejudice and discrimination ensure the continuance of inequality in the United States. Even subconsciously, we are furthering inequality through our actions and reactions to others… Because these forces are universally present in our daily lives, the way we use them or reject them will determine how they affect us.”

Anderson explains the direct relationship between prejudice and discrimination. He also gives examples of these occurrences in the past (blacks and whites segregation) and modern times (sexism, racism, etc.)

He delves into society’s fault for playing the “blame game” and choosing to ignore each other’s perspectives, leading to stereotypes. He also talks about affirmative action committees that serve to protect minorities.

“Something important to point out is that there is prejudice when it comes to people of lower class or economic standing, there are stereotypes that label them as untrustworthy, lazy, and even dangerous. This thought is fed by the just-world phenomenon, that of low economic status are uneducated, lazy, and are more likely to be substance abusers, and thus get what they deserve.”

Ibarra recounts how she discovered Pixar’s Luca and shares what she thought of the animation, focusing on how the film encapsulates socioeconomic discrimination in its settings. She then discusses the characters and their relationships with the protagonist. Finally, Ibarra notes how the movie alluded to flawed characters, such as having a smaller boat, mismatched or recycled kitchen furniture, and no shoes. 

The other cast even taunts Luca, saying he smells and gets his clothes from a dead person. These are typical things marginalized communities experience in real life. At the end of her essay, Ibarra points out how society is dogmatic against the lower class, thinking they are abusers. In Luca, the wealthy antagonist is shown to be violent and lazy.

“Even though the problem of discrimination has calmed down, it still happens… From these past experiences, we can realize that solutions to tough problems come in tough ways.”

The author introduces people who called out discrimination, such as Mahatma Gandhi, Dr. Martin Luther King Jr., and Barbara Henry – the only teacher who decided to teach Ruby Bridges, despite her skin color. 

He then moves on to mention the variations of present-day discrimination. He uses Donald Trump and the border he wants to build to keep the Hispanics out as an example. Finally, Bill ends the essay by telling the readers those who discriminate against others are bullies who want to get a reaction out of their victims. 

Do you get intimidated when you need to write an essay? Don’t be! If writing an essay makes you nervous, do it step by step. To start, write a simple 5 paragraph essay .

Prompts on Essays About Discrimination

Below are writing prompts that can inspire you on what to focus on when writing your discrimination essay:

Have you had to go through an aggressor who disliked you because you’re you? Write an essay about this incident, how it happened, what you felt during the episode, and what you did afterward. You can also include how it affected the way you interact with people. For example, did you try to tone down a part of yourself or change how you speak to avoid conflict?

List ways on how you can participate in lessening incidents of discrimination. Your list can include calling out biases, reporting to proper authorities, or spreading awareness of what discrimination is.

Is there an ongoing prejudice you observe in your school, subdivision, etc.? If other people in your community go through this unjust treatment, you can interview them and incorporate their thoughts on the matter.

Tackle what victims of discrimination have to go through daily. You can also talk about how it affected their life in the long run, such as having low self-esteem that limited their potential and opportunities and being frightened of getting involved with other individuals who may be bigots.

For this prompt, you can choose a subtopic to zero in on, like Workplace Discrimination, Disability Discrimination, and others. Then, add sample situations to demonstrate the unfairness better.

What are your thoughts on the different game rules for men and women? Do you believe these rules are just? Cite news incidents to make your essay more credible. For example, you can mention the incident where the Norwegian women’s beach handball team got fined for wearing tops and shorts instead of bikinis.

Since we learn to discriminate because of the society we grew up in, it’s only normal to be biased unintentionally. When you catch yourself having these partialities, what do you do? How do you train yourself not to discriminate against others?

Focus on an area of discrimination and suggest methods to lessen its instances. To give you an idea, you can concentrate on Workplace Discrimination, starting from its hiring process. You can propose that applicants are chosen based on their skills, so the company can implement a hiring procedure where applicants should go through written tests first before personal interviews.

If you instead want to focus on topics that include people from all walks of life, talk about diversity. Here’s an excellent guide on how to write an essay about diversity .

Maria Caballero is a freelance writer who has been writing since high school. She believes that to be a writer doesn't only refer to excellent syntax and semantics but also knowing how to weave words together to communicate to any reader effectively.

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Essay on Social Discrimination

Students are often asked to write an essay on Social Discrimination in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Social Discrimination

What is social discrimination.

Social discrimination is when people are treated differently based on things like their race, gender, religion, or disability. It can happen in many ways, like being denied a job or being made fun of. It can make people feel bad about themselves and make it hard for them to live their lives.

Types of Social Discrimination

There are many types of social discrimination, including:

• Racism: Treating people differently because of their race or skin color.
• Sexism: Treating people differently because of their gender.
• Religious discrimination: Treating people differently because of their religion or beliefs.
• Disability discrimination: Treating people differently because of their disability.

Effects of Social Discrimination

Social discrimination can have many negative effects on people, including:

• Lower self-esteem
• Health problems
• Difficulty getting a job or education

Social discrimination is a serious problem that can have many negative effects on people’s lives. It is important to be aware of discrimination and to speak out against it. We need to create a more just and equal society where everyone is treated with respect.

250 Words Essay on Social Discrimination

Social discrimination: what it means.

Social discrimination refers to unfair treatment of a person or group based on their differences. People can be discriminated against for various reasons, such as race, gender, religion, disability, or social status.

There are three main types of social discrimination:

• Institutional discrimination : This type of discrimination is built into and supported by laws, systems, and institutions. For example, in some countries, indigenous people are often denied the right to own land or receive an education.
• Interpersonal discrimination : This type of discrimination occurs between individuals. For example, when someone is bullied or treated unfairly at school or at work.
• Internalized discrimination : This type of discrimination is when people internalize negative messages about their own group and come to believe them. For example, a person may feel ashamed of their race or gender because they have been told it is inferior.

Consequences of Social Discrimination

Social discrimination has many negative consequences. For example, discrimination can lead to:

• Poverty and unemployment
• Poor health and well-being
• Educational disparities
• Limited access to justice
• Violence and hate crimes

Overcoming Social Discrimination

Overcoming social discrimination requires a collective effort. One important step is to raise awareness about discrimination and challenge discriminatory attitudes and behaviors. Another important step is to create more inclusive societies and promote diversity. Finally, it is important to have laws and policies that protect people from discrimination.

Social discrimination is a serious problem with severe consequences for both individuals and society as a whole. It is important to raise awareness about discrimination and to take action to create more inclusive and just societies.

500 Words Essay on Social Discrimination

Social discrimination: a deeper look.

Social discrimination refers to the unfair treatment of individuals based on their membership in a particular social group. This mistreatment may be stemmed from aspects like race, gender, religion, disability, and more.

Forms of Social Discrimination

Social discrimination can manifest in various forms, such as:

• Stereotyping: Assigning fixed and often negative traits to an entire social group.
• Prejudice: Holding unfavorable attitudes and beliefs towards members of a particular group.
• Racism: Prejudice and discrimination directed at individuals based on their race.
• Sexism: Prejudice and discrimination directed at individuals based on their gender.
• Xenophobia: Prejudice and discrimination directed at individuals based on their nationality or origin.

Consequences of Discrimination

Social discrimination can have devastating consequences for individuals and society as a whole. It can lead to:

• Limited opportunities: Discrimination can limit access to education, employment, and other opportunities.
• Social isolation: Discrimination can result in individuals being excluded from social activities and participation.
• Health issues: Discrimination can lead to stress, depression, and other health problems.
• Conflict and violence: Discrimination can create tensions and conflicts between different groups, sometimes leading to violence.

Combating Discrimination

Combating social discrimination requires collective effort and commitment to fostering a more just and equitable society. Some key steps include:

• Education and awareness: Raising awareness about discrimination and its consequences is crucial for changing attitudes and behaviors.
• Anti-discrimination laws and policies: Implementing laws and policies that prohibit discrimination and promote equality is essential for creating a legal framework for justice.
• Inclusive practices: Promoting inclusive practices in workplaces, schools, and communities helps to create environments where everyone feels respected and valued.
• Celebrating diversity: Celebrating the richness and beauty of diverse cultures and backgrounds helps to promote understanding and appreciation among people from different groups.

Social discrimination is a pervasive societal issue that requires attention and action. By working together, we can create societies where everyone is treated with dignity and respect, regardless of their background or identity.

That’s it! I hope the essay helped you.

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• Published: 10 September 2019

Effects of early adversity and social discrimination on empathy for complex mental states: An fMRI investigation

• Melike M. Fourie   ORCID: orcid.org/0000-0003-4879-7250 1 ,
• Dan J. Stein 2 ,
• Mark Solms 3 ,
• Pumla Gobodo-Madikizela 1 &
• Jean Decety   ORCID: orcid.org/0000-0002-6165-9891 4  

Scientific Reports volume  9 , Article number:  12959 ( 2019 ) Cite this article

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• Stress and resilience

There is extensive evidence of an association between early adversity and enduring neural changes that impact socioemotional processing throughout life. Yet little is known about the effects of on-going social discrimination on socioemotional functioning. Here we examined how cumulative experiences of social discrimination impact brain response during empathic responding—a crucial issue in South Africa, given its historical apartheid context and continuing legacies. White and Black South Africans completed measures of social adversity (early adversity and social discrimination), and underwent fMRI while viewing video clips depicting victims and perpetrators of apartheid crimes. Increased neural response was detected in brain regions associated with cognitive rather than affective empathy, and greater social adversity was associated with reduced reported compassion across participants. Notably, social discrimination (due to income level, weight, gender) in White participants was associated with increased amygdala reactivity, whereas social discrimination (due to race) in Black participants mediated the negative associations of temporoparietal junction and inferior frontal gyrus activation with compassion during emotionally provocative conditions. These findings suggest that (i) social discrimination has comparable associations at the neural level as other psychosocial stressors, and that (ii) the mechanisms underlying empathic responding vary as a function of the type of social discrimination experienced.

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Introduction

Early social experiences critically shape the neural circuitry underpinning social and emotional behavior throughout life 1 . For example, converging evidence suggests that early adversity, such as physical or emotional abuse, is associated with enduring changes in brain structure and function in circuits that facilitate stress responsivity, including the amygdala and anterior cingulate cortex (ACC), giving rise to alterations in emotional processing and regulation 2 , 3 , 4 . In contrast to this rich literature, our understanding of the impact of experiences of racism and social discrimination on brain functioning in marginalized groups remains poor, despite growing evidence of the immense toll of chronic experiences of such discrimination (actual and perceived) on physical and mental well-being 5 , 6 , 7 , 8 .

Perceived discrimination is an enduring form of psychosocial stress with cumulative effects on health 9 , 10 . Although overt racial discrimination may be declining worldwide, racial disparities are maintained by subtler forms of discrimination (e.g., ingroup favouritism, microaggression), which appears to be equally consequential in terms of psychological consequences 11 , 12 , 13 . Moreover, research is only beginning to unravel the neural mechanisms underlying the intergenerational transmission of systematic and long-standing forms of discrimination and collective violence 14 , 15 .

These are important issues for South Africa—more than two decades after its first democratic elections, the wounds and historical divisions resulting from gross human rights violations during apartheid remain deeply entrenched there. While much research has focused on intergroup responding in post-conflict countries, here we wanted to explore how cumulative experiences of social adversity impact neural circuitry underlying one of the most critical of human sensitivities, namely the ability to empathize. Notably, empathy can facilitate psychological repair when people bear witness to trauma testimonies, including expressions of forgiveness and remorse 16 . If the mechanisms underlying empathy are disrupted, this could be costly not only at the societal level of relating to and caring for others 17 , but also at the individual level of regulating personal distress 18 .

Previous behavioral work suggests that psychosocial stress, including early and collective experiences of adversity, are associated with impaired empathy 19 , 20 , 21 , 22 . To extend such findings, we used fMRI to examine empathy for complex mental states in a sample of Black and White South Africans with no history of mental illness who experienced apartheid. Specifically, we examined empathy in response to ecological video clips depicting victims (forgiving/ unforgiving) and perpetrators (apologetic/unapologetic) of apartheid crimes, and determined the extent to which experiences of early adversity and social discrimination are associated with altered responses in neural circuits involved in cognitive and affective empathy. We speculated that these different social stressors may converge via shared psychological mechanisms impacting overlapping neural circuits, particularly in marginalized populations living in environments with complex social challenges, such as poverty, racial stratification, negative social connectedness, and early exposure to these conditions 23 .

Theoretical conceptualisations of empathy emphasize two complementary, yet dissociable, pathways to share and understand others’ emotional states: (1) a mechanism for affect sharing including the dorsal anterior cingulate cortex (dACC), anterior insula (aINS), amygdala and brainstem, and (2) a more reflective and cognitively effortful process using mentalizing capacities 24 , 25 , 26 , 27 , 28 . Whereas observing others in physical pain reliably activates the affective empathy network 29 , 30 , observing others in emotional distress more consistently recruits areas associated with mentalizing, including dorsal and ventral medial prefrontal cortex (dmPFC and vmPFC), temporoparietal junction (TPJ), posterior superior temporal sulcus (pSTS), precuneus, and posterior cingulate cortex (PCC) 31 , 32 , 33 , 34 . While the contribution of affective and cognitive processes likely varies depending on the empathy-eliciting situation 35 , it is plausible that the more effortful cognitive mechanism, which also taps into memory and other self-reflective processes, is readily engaged in response to complex mental states 31 , 36 .

Early adversity has been found to be associated with more reactive, and less sensitive empathic engagement 37 , 38 . At the anatomical level, early adversity has consistently been associated with gray matter volume changes in the hippocampus and amygdala (areas with high densities of glucocorticoid receptors involved in the stress response), as well as with frontolimbic circuitry irregularities, including heightened activation of the ACC, orbitofrontal cortex, and amygdala (particularly on the right) 2 , 39 , 40 , 41 , 42 . Early adversity affects a broader network of structures, which have received comparatively less attention. Notably, recent research documents increased gray matter volume or heightened responsivity to socioaffective cues in areas associated with social information processing, such as the posterior STS/TPJ, temporal pole, inferior frontal gyrus (IFG), and precuneus following early adversity 4 , 43 , 44 , 45 . It has been argued that such higher-order association cortices may be particularly vulnerable to the long-term effects of early adversity due to their protracted postnatal development 3 .

With regard to experiences of social discrimination, only a handful of studies have investigated the downstream biological structural and functional changes that may contribute to race-based health disparities 46 , 47 , 48 . Initial evidence from these studies parallels that of other psychosocial stressors, suggesting that social discrimination is associated with functional alterations in activation and connectivity of the amygdala and ACC, in particular 49 . These areas form part of the paralimbic “salience network” (SN), which coactivates in response to varied forms of personal salience 50 , 51 . On this account, the cumulative effects of discriminatory events might result in heightened sensitivity to racial issues, altered salience attribution, and hence increased susceptibility to everyday stressors. Nevertheless, no study has investigated how chronic experiences of different types of discrimination impact real-life empathic responding.

First, the present study examined the neural response associated with empathy for complex mental states using ecological stimuli in adult Black and White South Africans. We hypothesized that neural activation in response to forgiving/unforgiving victims and apologetic/unapologetic perpetrators would be characterized by more effortful cognitive perspective taking, and thus activation in mentalizing areas (dmPFC, TPJ, precuneus), rather than by affective sharing mechanisms. Furthermore, we anticipated significantly higher reported compassion (empathic concern) for victims than for perpetrators 52 , particularly unapologetic perpetrators.

Second, we examined whether neural processing of material reminiscent of apartheid atrocities would be processed differently in Black compared to White participants, given that Black individuals endured historical oppression and marginalization. It was predicted that Black participants would respond with greater empathic arousal (sensitivity), as the evocative video clips might be of heightened personal relevance and emotional salience to them. Hence, we expected elevated activation in the affective empathy or “salience network” 49 , including the dACC, aINS, and other subcortical and limbic structures 53 .

Finally, we examined associations between specific forms of social adversity (early adversity and social discrimination) and reported compassion, as well as functional changes in cognitive and affective empathy mechanisms. In light of previous findings, we expected social adversity to be associated with reduced reported compassion 19 . At the neural level, we hypothesized that it would be associated with heightened activation in stress-related circuitry (amygdala) and areas involved in social information processing (temporal regions and IFG), thus affecting key areas associated with both cognitive and affective empathy. Because of the relative dearth of studies investigating neural alterations associated with social discrimination, our hypotheses are exploratory in nature.

Behavioral data

Statistical analyses were carried out using IBM SPSS Statistics for Windows, version 25.0 (IBM Corp., Armonk, N.Y., USA).

Emotion ratings

Post-scan emotion ratings were subjected to repeated-measures factorial ANOVAs (Fig.  1 , Supplementary Table  S3 ). In instances where the assumption of sphericity was violated, the degrees of freedom were adjusted using Greenhouse-Geisser epsilon corrections. We first examined the four conditions of interest, namely victim forgiving (VF), victim unforgiving (VU), perpetrator apologetic (PA), and perpetrator unapologetic (PU), using a 4 (Condition: VF, VU, PA, PU) × 5 (Emotion: compassion, moral indignation, personal distress, guilt, shame) repeated-measures factorial ANOVA. The main effect of condition was not significant ( p  = 0.11), but that of emotion was, F (2.92, 102.34) = 41.35, p  < 0.001, ε  = 0.73. Planned contrasts indicated that compassion was rated as significantly higher than all other emotions across conditions ( p s < 0.001, r s > 0.73). The 2-way interaction was also significant, F (5.36, 187.68) = 13.00, p  < 0.001, ε  = 0.45.

Subjective emotion ratings for participants in response to the 4 conditions of interest: victim forgiving (VF), victim unforgiving (VU), perpetrator apologetic (PA) and perpetrator unapologetic (PU). Note that anger represents moral indignation. Error bars indicate standard error of the mean. * p  < 0.05. ** p  < 0.01. *** p  < 0.001.

Compassion was explored using a participant group × condition mixed factorial ANOVA. The main effect of participant group was significant ( F (1, 33) = 7.05, p  = 0.01), with Black participants’ ratings higher than those of White participants. The main effect of condition was also significant, F (2.06, 67.99) = 14.15, p  < 0.001. As anticipated, contrasts indicated that compassion for the PU condition was significantly lower than all other conditions across participants ( p s < 0.001, r s > 0.55), while compassion for the VF, VU, and PA conditions did not differ ( p s > 0.16). A similar analysis for moral indignation indicated that the main effect of participant group was significant ( F (1, 33) = 4.94, p  = 0.03), with Black participants’ ratings higher than those of White participants. The main effect of condition was also significant, F (2.18, 72.07) = 15.26, p  < 0.001. Contrasts indicated that ratings for the PU condition were significantly higher than those of the VF, PA ( p s < 0.001, r s > 0.61) and VU ( p  = 0.03, r  = 0.36) conditions, while the VU condition was rated higher in moral indignation than the VF and PA conditions ( p s = 0.001, r s > 0.52). For personal distress, the main effect of condition was significant ( p  < 0.01), with contrasts indicating that PU and VU conditions were rated as higher than other conditions ( p s < 0.05, r s > 0.35). Finally, for both guilt and shame, only the interactions between participant group and condition were significant ( F s > 4.40, p s < 0.01), with contrasts revealing that White participants’ ratings were significantly higher than those of Black participants for PU compared to other conditions ( p s < 0.05, r s > 0.32).

Post-hoc analysis confirmed that emotion ratings did not differ significantly as a function of victim or perpetrator race for either Black or White participants (see Supplementary Results).

Questionnaire measures

We detected significant negative correlations between participants’ reported social discrimination and compassion ratings in response to the VF, VU, and PA conditions ( r 35(2-tailed )  > −0.43, p s < 0.01) (see Supplementary Table  S4 ).

While everyday discrimination scores were higher for Black than White participants, this difference was not significant ( p  = 0.13). There were significant qualitative differences in the perceived reasons for discrimination, however. Black participants reported race as the main reason (72%), whereas White participants reported gender (22%), income level (22%), and weight/appearance (22%), rather than race (17%), as the main reasons (Table  1 ).

We also found significant negative correlations between participants’ childhood trauma (total and emotional abuse) scores and their compassion ratings in response to the VF and PA conditions ( r 35(2-tailed )  > −0.43, p s < 0.01) (see Supplementary Table  S4 ). No significant differences for early adversity were reported by White and Black participants ( p s > 0.16) (Table  1 ).

In general, greater social discrimination and early adversity, particularly emotional abuse, were associated with reduced reported compassion.

Whole-brain contrasts

The contrasts for the 4 conditions of interest against the neutral condition revealed increased activity predominantly in areas associated with mentalizing (see Fig.  2 , Supplementary Tables  S5 – S8 ). Specifically, all conditions showed heightened activity in the middle temporal gyrus, temporal pole, precuneus, and IFG. Heightened activity in TPJ, dmPFC and premotor cortex was additionally observed for the VF, VU, and PU conditions. Finally, the PU condition also showed significant activation in the periaqueductal gray (PAG), amygdala, and dorsal striatum.

Whole-brain contrasts for the 4 conditions of interest (VF, VU, PA, and PU) against the neutral condition displayed on sagittal (left) and axial (right) sections in Talaraich space (p  < 0.005 corrected for multiple comparisons using Monte Carlo cluster-level thresholding).

Whole-brain main effect of participant group

Next, to examine group differences in empathic responding, we looked at the main effect of participant group. Here we observed increased activity in ventromedial prefrontal cortex (vmPFC), dACC, left dorsolateral prefrontal cortex (dlPFC), right pSTS, inferior parietal lobe, and thalamus (see Supplementary Table  S9 ).

Beta values extracted from these functionally defined ROIs and analyzed by 2-way participant group × condition ANOVAs confirmed significant main effects for participant group ( F s > 21.50, p s < 0.001), such that activation in all areas were greater for Black compared to White participants (Fig.  3 , Supplementary Table  S10 ). We also found a significant interaction in the right pSTS ( F (4, 136) = 4.03, p  = 0.004), qualified by the fact that signal increase from neutral (Neu) to VU and Neu to PU conditions was greater for Black than White participants ( p s < 0.01, r s > 0.44).

Regions showing a significant main effect of participant group (left panel). Activation in the right pSTS, vmPFC, dACC, and left dlPFC were significantly greater for Black compared to White individuals ( p  < 0.005 corrected for multiple comparisons using Monte Carlo cluster-level thresholding). Parameter estimates (betas) reflect the average signal intensity for each cluster for each condition (right panel). Error bars indicate standard error of the mean. dACC: dorsal anterior cingulate cortex, dlPFC: dorsolateral prefrontal cortex, pSTS: posterior superior temporal sulcus, vmPFC: ventromedial prefrontal cortex. ** p  < 0.01. *** p  < 0.001.

Independent ROI analyses

We assessed extracted beta values in ROIs using 2-way participant group × condition ANOVAs (Supplementary Table  S11 ).

Affective empathy areas: No significant main effects of condition were observed for dACC or aINS. Consistent with the above, we found only a significant main effect of participant group in dACC ( F (1, 34) = 6.90, p  = 0.01), with activation greater for Black compared to White participants. No interaction effects were found.

ROI analyses for amygdala revealed a significant main effect of condition ( F s > 4.00, p s < 0.005). Planned contrasts indicated that activation during the PU condition was significantly higher than activation during the Neu, VF, and PA (but not VU) conditions for both left ( p s < 0.003, r s > 0.48) and right ( p s < 0.02, r s > 0.38) amygdala (Fig.  4 ).

Amygdala reactivity. ( a ) Image shows ROI peak voxels defined based on independent data 29 . ( b ) Parameter estimates (betas) reflect the average signal intensity in the left and right amygdala in response to the various experimental conditions. ( c ) Higher activation in the right amygdala was associated with reduced compassion ratings in response to the PU condition across participants. Error bars indicate standard error of the mean. VF: victim forgiving, VU: victim unforgiving, PA: perpetrator apologetic, PU: perpetrator unapologetic. ** p  < 0.01.

Mentalizing areas: We detected significant main effects of condition for the dmPFC, bilateral IFG, bilateral TPJ, and precuneus ( F s > 5.0, p s < 0.001). Planned contrasts indicated that, for all areas, activation during the four conditions of interest was significantly higher than the Neu condition ( p s < 0.05, r s > 0.32). In addition, activation during the PU condition was significantly higher than all other conditions for the left IFG ( p s < 0.04, r s > 0.35), and higher than the PA condition for the right IFG, left and right TPJ, and precuneus ( p s < 0.05, r s > 0.36). No other contrasts reached significance.

Brain-behavior correlations

Correlation data are presented in Supplementary Table  S12 .

Compassion: Across participants, higher right amygdala activation during the PU condition was associated with reduced compassion ratings in response to PU clips ( r 35(2-tailed)  = −0.45, p  = 0.006) (Fig.  4 ). No significant correlations were observed for other affective empathy areas.

Regarding mentalizing areas, in Black participants, higher left TPJ activation during the VU condition was associated with reduced compassion ratings in response to VU clips ( r 18(2-tailed )  = −0.53, p  = 0.03). Similarly, higher left IFG activation during the VU and PU conditions were associated with reduced compassion ratings in response to VU and PU clips, respectively ( r s > −0.50, p s < 0.05). No corresponding significant correlations were observed for White participants.

Social discrimination and early adversity: In White participants, everyday discrimination scores were associated with increased activation in the right amygdala in response to VU, PA and PU conditions ( r s > 0.54, p s < 0.03), and left amygdala in response to VF and PA conditions ( r s > 0.50, p s < 0.04). No significant correlations were observed for other affective empathy areas.

Regarding mentalizing areas, in Black participants, higher everyday discrimination scores were associated with increased activation in the left TPJ in response to the VU condition ( r 18(2-tailed )  = 0.67, p  = 0.003). Furthermore, higher everyday discrimination scores were associated with increased activation in the left IFG in response to VU and PU conditions ( r 18(2-tailed)  = 0.64, p  = 0.005 and r 18(2-tailed)  = 0.56, p  = 0.02, respectively). Similarly, higher CTQ emotional abuse scores were associated with increased activation in the left TPJ in response to all four conditions ( rs  > 0.57, p  < 0.02). Again, no corresponding significant correlations were observed for White participants.

To explain the mechanism underlying some of these observed relationships, we conducted exploratory mediation analyses (Fig.  5 ) 54 . These analyses suggested that for Black participants, everyday discrimination and CTQ emotional abuse, two highly correlated constructs ( r  = 0.78, p  < 0.001), each mediated the negative relationship between compassion ratings and activation in the left TPJ for the VU condition: Everyday discrimination and CTQ emotional abuse scores predicted neural activity while controlling for compassion ratings (βs = 0.54, p s < 0.03), but rendered nonsignificant the negative relationship between compassion ratings and left TPJ activation (βs < −0.32, p s > 0.12), indicating full mediation (Sobel test p s < 0.05). Everyday discrimination also fully mediated the negative relationship between compassion ratings and activation in the left IFG for the VU condition (β = 0.54, p  = 0.02, while controlling for compassion ratings). While these effects were strongest for the VU condition, a similar mediation effect in the left IFG for everyday discrimination was also observed for the PU condition (see Supplementary Table  S13 ).

Mediation analysis for the victim unforgiving (VU) condition. ( a ) Both social discrimination (Everyday Discrimination scores) and early adversity (CTQ Emotional Abuse scores) mediated the negative relationship between reported compassion and left TPJ activation ( R 2  > 0.48, p s < 0.01). ( b ) Social discrimination also mediated the negative relationship between reported compassion and left IFG activation ( R 2  = 0.46, p  = 0.01). Sobel tests showed that, in each case, including the mediator in the model significantly reduced the effect of compassion on brain activation. Scatter plots show the correlations (Pearson’s r , 2-tailed) between b r ain activation in the left TPJ/IFG and reported compassion and social adversity for Black and White participants, respectively. Brain images show corresponding ROIs based on independent data 61 . * p  < 0.05. ** p  < 0.01.

Whereas sustained neural effects of early adversity are well documented, our understanding of how enduring forms of social discrimination impact brain functioning remains poor. The present research investigated the effects of early adversity and social discrimination on neural response when White and Black South Africans empathized with apartheid victims and perpetrators. Overall, the findings suggest that Black compared to White participants responded with heightened empathic sensitivity. In addition to greater reported compassion when witnessing victim and perpetrator testimonies, their neural responses were characterized by heightened activation in areas associated with generation of affective meaning and salience (vmPFC, dACC), as well as with mental state representation (pSTS). Greater experiences of social adversity (early adversity and social discrimination) were associated with reduced reported compassion across participants. In White participants, social discrimination (primarily because of income level/weight/gender) was associated with undifferentiated amygdala reactivity. By contrast, in Black participants, social discrimination (primarily because of race) and early adversity mediated the negative relationships between brain activation (TPJ, IFG) and compassion for unforgiving and unapologetic individuals. These findings suggest that areas involved in stress-related circuitry and social information processing are impacted differentially in terms of empathic responding in people who have endured cumulative experiences of social adversity.

Consistent with our first hypothesis, the findings confirmed that empathy in response to complex mental states is associated more with effortful cognitive perspective taking (i.e., heightened activation in temporal areas, precuneus, IFG, and dmPFC) than with affective empathy mechanisms. Here, the complexity of targets’ mental states, combined with the age of participants, and the explicit instruction to imagine how victims/perpetrators experienced their situation, likely facilitated cognitive perspective taking 28 , 55 . Likewise, lack of significant dACC and aINS activation might be explained by the nature of our task: previous empathy research often made use of highly salient, brief stimuli that are associated with a detecting/orienting response not specific to pain perception 56 , 57 . Nevertheless, recent research suggests that empathic accuracy is more dependent on cognitive perspective taking than affective sharing 58 .

Interestingly, viewing of unapologetic perpetrators was associated with (i) significant amygdala and PAG activation (whole-brain analysis), (ii) the highest left IFG activation (ROI analysis), and (iii) behaviorally, with the lowest compassion and highest moral indignation ratings. Consistent with these findings, elevated right amygdala activation during the PU condition was associated with reduced compassion ratings across participants. Because the amygdala is sensitive to negative, threat-related stimuli 59 , and involved in evaluating high-level social information, such as trustworthiness 60 , these results are consistent with previous research.

The IFG has been associated with a host of cognitive capacities, most notably mentalizing 61 , 62 , but also emotion regulation 63 and understanding 64 . However, the IFG likely plays a more general role in cognition 65 . Tops and Boksem describe the importance of the IFG in ventral corticolimbic control pathways that manage attention and behavior in situations with low predictability 66 , 67 . Accordingly, the IFG supports a reactive kind of behavioral control that is engaged when attentional focus is on urgent events discrepant with expectancies, thus recruiting greater evaluative processes to guide behavior. While this interpretation relies on reverse inference, we believe that, using a likelihoodist approach, it is more probable than interpretations focusing on emotion understanding 65 , 68 .

Consistent with our second hypothesis, we observed significant group differences. Black compared to White participants responded with greater self-reported compassion (and moral indignation) to the video clips, and at a neural level their responses were characterized by heightened activation in dACC, thalamus, vmPFC, dlPFC and pSTS. As detailed below, one interpretation of these findings is increased empathic sensitivity, but also greater subjective valuation and overt mental state representation for Black versus White participants.

The dACC and thalamus form part of the paralimbic salience network, which directs attention to events of personal importance (both internal and external), thereby determining selection for in-depth processing 50 , 69 . The dACC, in particular, may play an important role in indexing socially pertinent information and integrating motivationally relevant information with downstream physiological reactions 70 , 71 . Hence, the dACC is also crucial for empathic sensitivity—increased arousal when witnessing another in distress may be associated with more physiological signals to help interpret the target’s emotional state and potentially promote empathic concern 17 . By comparison, White participants’ responses may be more indicative of emotional blunting. A previous fMRI study found that White participants’ reaction of guilt, and especially shame, in response to Truth and Reconciliation Commission (TRC) footage were associated with reduced activation in areas associated with affective empathy 31 . Likewise, enhanced guilt and shame reported here by White participants might have promoted a more egocentric focus and disengagement from the shame-inducing stimuli 72 , 73 .

The vmPFC has reciprocal connections with the amygdala and hypothalamus and has consistently been implicated in the regulation of emotional responsiveness and empathic concern 24 , 74 , 75 , 76 . Recent theorizing suggests the vmPFC is a core area encoding subjective value of social and non-social stimuli in a context and goal-dependent manner 77 , 78 . The vmPFC thus appears unnecessary for simple forms of affectivity, but essential for the generation of affective meaning to coordinate appropriate physiological emotional responses and decision-making. Generating affective meaning includes representing the affective qualities of an event, its value, similar past situations, and potential outcomes 79 . Thus, increased activity in vmPFC for Back participants when viewing the clips might imply heightened meaning-making/valuation, and by extension, empathic concern.

The pSTS appears involved differentially in the visual analysis and interpretation of socially salient cues, such as bodily motion and facial emotion, in evaluating goal-directed action, and in processing overt (versus covert) mental states 80 , 81 , 82 . In our task, facial expressions and bodily motion were typically more exaggerated in VU and PU conditions, which portrayed more anger/resentment than the other conditions and might explain the heightened pSTS activation. Importantly, heightened mental state representation for these conditions was not necessarily associated with heightened compassion, as the PU condition was associated with the lowest compassion and highest moral indignation ratings. Indeed, previous research suggests the mentalizing network activates robustly both in the presence and absence of harmful mental states 83 , 84 .

The above group differences can also be interpreted to support the hypothesis that social marginalization cumulatively impacts the neural response in ACC, resulting in altered salience attribution and hypervigilance with regard to race-related discrepancies 47 , 49 . Indeed, the high reported moral indignation and concomitant dlPFC activation suggest engagement of the appraisal system to self-regulate 48 . During post-experiment interviews, several Black participants reported that, in addition to “feeling sorry”, the video clips triggered anger for them at the lack of social justice and change since 1994. In this regard, research has shown that high ingroup empathy can trigger hostility toward an outgroup perceived to be the source of one’s suffering 85 , 86 . The present material likely resonated with both apartheid memory and its legacies in the present, initiating other self-reflective processes. Thus, in the current context, enhanced dACC activation might also reflect affective dissonance 87 , 88 .

In sum, while Black participants may experience heightened empathic sensitivity to emotionally charged material in the moment, frequently needing to exert emotional control could tax executive processes, potentially leaving individuals feeling distressed and vulnerable to subsequent stressors 48 , 89 .

Our third hypothesis concerned individual differences. The present finding that social discrimination and early adversity across participants were associated with reduced reported compassion is consistent with literature. Previous reports cite early adversity as being significantly associated with impaired empathy 19 , 20 , 21 , including difficulty caring for or taking the perspectives of others. Similarly, research suggests that chronic experiences of discrimination are typically experienced as stressful, and may undermine empathy 10 , 90 . Both anxious emotions, that enhance egocentrism, and heightened cognitive load have been shown to impact empathy-related processing adversely 91 , 92 .

Social adversity was also associated with functional alterations in areas associated with stress reactivity and social information processing. Specifically, in White participants, social discrimination was associated with heightened bilateral amygdala reactivity in response to all conditions. This finding is consistent with literature suggesting that perceptions of negative social treatment is associated with increased emotional reactivity and reduced specificity in response to socially salient stimuli 46 , 49 , 93 . Heightened amygdala reactivity has been associated with higher levels of current psychological stress as well as with stress-related mental disorders 94 , 95 , 96 .

By comparison, in Black participants, social adversity was associated with heightened activation in the left TPJ and IFG. Moreover, childhood emotional abuse, and especially social discrimination, mediated the negative relationships between reported compassion and brain activation in these structures for the VU and PU conditions. Whereas discrimination-associated altered responses in White participants were thus indicative of emotional reactivity, the findings for Black participants are consistent with the hypothesis that discrimination-associated heightened activity in higher-order social information processing areas allows more fine-tuned distinctions between conditions, and hence decreased compassion specifically towards unforgiving and unapologetic individuals.

Elevated activity in social information processing areas in adults who experienced early adversity have previously been proposed to reflect strategies that compensate for deficits in emotional empathy by recruiting more effortful cognitive empathy 44 , 45 . However, in the present context, heightened activity in TPJ and IFG were associated with appropriately reduced compassion. A more likely explanation, then, is that these responses reflect increased identification and representation of negative social cues 43 . Early detection of threatening stimuli, particularly angry emotions, is adaptive in contexts of childhood adversity 97 . Likewise, individuals who regularly experience race-based rejection are more vigilant with regard to environmental threats 23 .

The TPJ and IFG (together with the STS and inferior parietal lobe) form a large part of the ventral attention network, which is sensitive to behaviorally relevant, novel events 98 , 99 . According to predictive and reactive control systems theory (PARCS), response biases in this network may result from long-term exposure to unpredictable environments, such as those associated with inconsistent parenting and trauma, and may suggest a more reactive kind of behavioral control guided by momentary environmental stimuli 66 , 67 , 100 . If sustained, this mode of behavioral control might predispose the individual to anxious anticipation of negative events and potentially mood disturbances 100 , 101 . Yet in the current context, response biases were not generalized, but elevated in response to the more emotionally charged and unpredictable conditions, potentially reflecting adaptive responses to meet current challenges.

Given that both early adversity (emotional abuse) and social discrimination were associated with elevated activation in areas associated with reactive control, it should be noted that these constructs were highly correlated and explained a common variance regarding brain activation responses. As noted, this might be due to early adversity and social discrimination often coinciding in marginalized populations, but also because those who suffered early experiences of abuse might be more vulnerable to social mistreatment 102 , 103 . In the absence of longitudinal data, any causal explanations remain speculative.

A key question is why, given the comparable levels of reported social adversity across participant groups, responses differed at a neural level. For early adversity, the macro context of White participants is perhaps more protective in terms of resources than those of economically marginalized Black participants, possibly buffering the negative effects of maltreatment 104 . For social discrimination, however, the explanation likely involves qualitative differences in perceived discrimination: race for Black participants; income level, gender, and weight for White participants. Research suggests that appraisals of negative social treatment and coping mechanisms vary as a function of the type of discrimination 102 , 105 . Experiences of negative social treatment may furthermore be more distressing for disadvantaged than advantaged groups, because their attributions of prejudice are likely to be more internalized and stable over time 106 , 107 . On the other hand, factors such as income level and weight are perceived to be under personal control. Group memberships based on such characteristics may be associated with greater self-stigma, which is more isolating and related to poorer well-being than memberships based on factors beyond personal control 8 , 108 . Indeed, pervasive discrimination against members of disadvantaged groups may result in strong connections with fellow group members, which serves as a coping mechanism to counter psychological distress 109 , 110 . What seems to matter, then, is whether group identity has positive or negative value for the individual 6 .

A few limitations of this study deserve emphasis. First, our design does not allow causal inferences to be made from the observed associations of social adversity. While only correlations with large effect sizes were reported, they should be interpreted with caution due to the relatively small sample size. Our data were obtained from a non-clinical sample and hence cannot prove increased risk towards subsequent socioaffective disturbances in either White or Black participants. The data also do not allow us to comment on whether alterations in functional activity are associated with enduring structural changes.

Second, because we relied partially on reverse inference to infer psychological processes from observed patterns of brain activation, the inductive validity of these inferences may be questioned 111 . Future research could minimize such concerns by recasting reverse inferences in likelihoodist terms where applicable, as we have (i.e., deciding which of two competing hypotheses is best supported by the data). This approach has been proposed to circumvent the issues associated with reverse inference 68 . In addition, future enquiries would benefit from including neuroimaging tasks with interpretable behavioral evidence that manipulate specific psychological processes, such as subjective valuation, mental state representation, and self-regulation, which would validate inferences by enhancing the construct validity of neural responses beyond our passive viewing paradigm 112 .

Finally, because marginalization is not restricted to discrete experiences, but manifests in the pervasive, ongoing, systematic, structural violence of the entire social space, it may be subject to underreporting 49 . Furthermore, individual differences in attributing negative experiences to discrimination (e.g., vigilance versus minimization bias) 113 , may limit replication of our results. It is thus possible that elevations in amygdala, TPJ, and IFG activity reflect innate response biases, rather than social discrimination per se.

In conclusion, our results elucidate the neural effects of social adversity in terms of functional changes in cognitive and affective empathy mechanisms. Whereas social discrimination in White participants was associated with greater amygdala reactivity suggesting altered stress responsivity, social adversity in Black participants was associated with increased activation in mentalizing and social information processing structures and decreased compassion to emotionally provocative conditions. These data extend the literature in two important ways: First, it shows that experiences of social discrimination have comparable associations at the neural level as other types of psychosocial stress. Second, it provides an initial framework for understanding how empathy might be modulated in those who endure cumulative experiences of social discrimination, with the nature of the stigma likely contributing significantly to altered neural response patterns. Beyond these more direct associations with social adversity, however, the present research also informs our understanding of the mechanisms that might impact empathic responding in members of high-status groups (e.g., emotional blunting) when they bear witness to trauma testimonies. More detailed investigation of these processes may ultimately be useful in facilitating psychological repair in the wake of historical trauma, and in moving towards more socially just and equal societies.

Participants

Thirty-six South Africans, 18 who identified as Black African (10 female, M  = 40.28 years, SD  = 4.17), and 18 who identified as White (8 female, M  = 40.83 years, SD  = 6.07), recruited through local newspaper advertisements, completed all study procedures and received ZAR200 compensation. All participants lived in South Africa during apartheid (prior to 1994) and obtained Grade 12 as a minimum level of education (Black African: M  = 16.28 years, SD  = 2.30; White: M  = 16.22 years, SD  = 3.04). Participants were without previously diagnosed neurological, cardiovascular, or psychiatric disorders, and none were clinically depressed 114 .

All participants provided informed consent. The study was approved by the University of Cape Town’s Human Research Ethics Committee and all procedures were carried out according to these guidelines.

Social discrimination

To assess relatively minor experiences of unfair treatment that contribute to a type of chronic stress, the Everyday Discrimination Scale was used 115 . This 9-item scale assesses the frequency of different forms of social mistreatment from 1 ( Never ) to 6 ( Almost Everyday ), and has been shown to have good internal validity in the South African context 116 . If participants experienced discrimination “ A few times a year ” or more frequently, they were asked to indicate what they considered the main reason(s) for these experiences were (e.g., race, gender, physical appearance, or income level).

Early adversity

The Childhood Trauma Questionnaire Short-Form (CTQ-SF) was used to assess the severity of child maltreatment 117 . This 25-item retrospective measure is used extensively in peer-reviewed research and has good validity across clinical and nonclinical populations 4 . It records the frequency of physical neglect, emotional neglect, physical abuse, sexual abuse, and emotional abuse when participants “were growing up”, ranging from 1 ( Never True ) to 5 ( Very Often True ).

Questionnaire measures were completed several weeks before scanning during an online survey.

Stimuli consisted of 50 short 6–9 s video clips (352 pixels × 288 pixels) featuring Black African or White individuals either as victims expressing (i) forgiveness (VF) or (ii) unforgiveness (VU), as perpetrators who are (iii) apologetic (PA) or (iv) unapologetic (PU), or as (v) individuals expressing neutral views (Neu). The verbal content of the clips were sourced from actual TRC hearings and related documentaries but were reproduced (standardized) for the purposes of this study using actors. Neutral clips included statements about everyday events unrelated to the TRC. Video clips were recorded with a digital color camcorder from a frontal view that included the actor’s whole head and parts of the shoulders. The actors were seated in front of a white background and were instructed to direct their gaze at a point about 30 cm to the left of the camcorder to avoid direct eye contact, giving the impression of being in conversation with an interviewer. All clips were validated by a mixed-race sample of 57 volunteers in an independent behavioral experiment (see Supplementary Material). During scanning, clips were presented three at a time in blocked format, separated by a 500 ms scrambled static grey image, and superseded by a 3 s title indicating the nature of the block (e.g., Victim Forgiving). Clips within each block were randomized and included at least one Black and one White individual. No clip was repeated more than once.

Participants received standardized instructions about the purpose of the study, namely to understand how the human brain responds to others’ distress. Specifically, they learned that they would watch short video clips of victims and perpetrators from the TRC hearings, who were interviewed again after the hearings and asked how they felt about past events. It was highlighted that victims were forgiving or unforgiving, whereas perpetrators were apologetic or unapologetic about their crimes. They were also told that some individuals expressed views unrelated to the TRC (i.e., neutral clips). To ensure participants understood the context of the TRC clips, they were shown a 2 min video about the TRC, as well as an example of a clip from each condition. They were instructed to empathize with individuals in each clip and to imagine how they felt in their situation.

In the scanner, participants viewed stimuli through a mirror system mounted to the head coil, which was displayed with E-Prime software, version 2.0 (Psychology Software Tools, Inc.). Participants first underwent a structural scan, followed by three runs of stimuli counterbalanced across participants. Each run consisted of 10 blocks (2 per condition), with the order of conditions randomized within a half run. The interval between blocks lasted 10 s during which participants fixated at a central cross.

Post-scan emotion ratings

After the scan, participants reported how much they felt distressed (personal distress), sorry (compassion/empathic concern), angry (moral indignation), guilty, and ashamed in response to each clip on separate 1 ( not at all ) to 9 ( extremely ) visual analog scales (VAS). Participants were then briefly interviewed about their experiences in the scanner and debriefed. They were also offered the opportunity to see a counsellor, should they wish to further discuss their experiences.

fMRI Image acquisition and data analysis

MRI data were acquired on a 3T Allegra system (Siemens, Erlangen, Germany). The high-resolution anatomical scan was acquired with a T 1 -weighted sequence (3D mprage, TR/TE = 2530/6.5 ms). Functional images covering the whole brain were acquired with a T 2 *-weighted echo-planar (EPI) imaging sequence using blood-oxygenation-level-dependent (BOLD) contrast (TR/TE = 2000/30 ms, slice thickness = 3 mm, gap = 0.9 mm, flip angle = 90°, field of view = 240 × 240 mm). The first four volumes of each run were discarded to allow for T 1 equilibration effects.

All fMRI analyses were performed using Brain Voyager QX, version 2.8 (Brain Innovation, Maastricht, Netherlands). Preprocessing of images included correction for slice acquisition times and linear trends, temporal filtering with a high-pass filter of 2 cycles/point, and motion-correction relative to the first volume of each run with trilinear/sinc interpolation. No run exceeded 3 mm displacement/3.0° rotation. Participants’ functional data sets were co-registered with their structural MRI and spatially normalized to Talaraich space.

Whole-brain group analyses were performed with a random effects analysis of variance using the general linear model (GLM) with predictors corresponding to known experimental blocks convolved by the standard haemodynamic response function. We defined predictors for the 5 conditions: VF, VU, PA, PU, and Neu. The six z-transformed motion correction parameters were added as predictors of no interest to reduce motion artifacts.

The resulting estimated beta values were entered into a second-level two-factor mixed factorial ANOVA, with the between-subjects factor participant group (Black vs. White), and the within-subjects factor condition (5 levels). To assess specific condition effects, we contrasted each of the four conditions of interest against the neutral condition. In addition, we evaluated the main effect of participant group. All whole-brain results are reported at P  < 0.005, corrected for multiple comparisons using the Monte Carlo cluster threshold estimation simulation tool implemented in Brain Voyager running1000 iterations 118 . To further explore these results, region of interest (ROI) analyses were performed for the activated clusters that emerged in the whole-brain main effect of participant group analysis (shown in Supplementary Table  S9 ). Random effects analysis of variance was performed on the average signal in each cluster for each participant using the GLM described above. Beta values generated by this analysis (reflecting the mean percent signal change for each condition) were analyzed by 2-way ANOVA.

To enhance the power of our analyses and reduce the Type-I error rate by performing fewer statistical tests 119 , we defined hypothesis-driven independent ROIs. Areas most consistently implicated in experience sharing (affective empathy: dACC, aINS) and mentalizing (cognitive empathy: dmPFC, TPJ, precuneus, and IFG), respectively, were defined based on recent meta-analyses (see Supplementary Table  S2 for coordinates) 29 , 61 . In addition, we selected ROIs for the amygdala, because previous studies investigating the neural sequelae of early adversity have consistently reported heightened amygdala reactivity during emotional processing 43 . All regions were defined as spheres with 8 mm radiuses centered at the peak voxel in each cluster, except for the amygdala, where spheres were defined with 5 mm radiuses. ROI analyses for these independently selected regions were conducted as described above, by computing beta estimates representing the average percent signal change for each region and analyzing by 2-way ANOVA.

To examine relationships between social adversity and brain activity, zero-order correlations between ROI beta values for our 4 conditions of interest and behavioral data (childhood maltreatment, everyday discrimination and compassion scores) were inspected. ROIs included in these analyses were those associated with affective and cognitive empathy, as well as the amygdala. To limit the number of correlations 120 , only correlation coefficients reflecting large effect sizes ( r ≥ ±0.50), or those significant at the 1% level (i.e., p  < 0.01) were interpreted and further inspected using 95% confidence intervals (CIs) derived through bootstrapping.

Data Availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work grew out of a larger study conceptualized by P.G.M. and M.S. to investigate empathy in the responses of Black and White participants to video clips from the South African Truth and Reconciliation Commission, based on data from psychoanalytic interviews and brain imaging. The research was supported by grants from the Fetzer Institute, the National Research Foundation (NRF) of South Africa, and the South African Medical Research Council (MRC).

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M.M.F., D.S. and J.D. designed the fMRI research and protocol. M.M.F. collected and analyzed all data and wrote and prepared the main manuscript text (including figures). D.S, M.S., P.G.M. and J.D. provided critical revisions. All authors approved the submitted version of the manuscript.

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Fourie, M.M., Stein, D.J., Solms, M. et al. Effects of early adversity and social discrimination on empathy for complex mental states: An fMRI investigation. Sci Rep 9 , 12959 (2019). https://doi.org/10.1038/s41598-019-49298-4

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A systematic review and meta-analysis of the Everyday Discrimination Scale and biomarker outcomes

Jourdyn a. lawrence.

a Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA

b Population Health Sciences, Harvard Graduate School of Arts and Sciences, Cambridge, MA, USA

c François-Xavier Bagnoud (FXB) Center for Health and Human Rights, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Ichiro Kawachi

Kellee white.

d Department of Health Policy and Management, University of Maryland School of Public Health, College Park, MD, USA

Mary T. Bassett

Naomi priest.

e Centre for Social Research and Methods, Australian National University, Canberra, Australian Capital Territory, Australia

f Murdoch Children’s Research Institute, Parkville, Victoria, Australia

Joan Gakii Masunga

g Center for Bioethics, Harvard Medical School, Boston, MA, USA

h Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA

Hannah J. Cory

i Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

j Countway Library, Harvard Medical School, Boston, MA, USA

David R. Williams

k Department of African and African American Studies, Harvard University, Cambridge, MA, USA

Associated Data

Discrimination has consistently been associated with multiple adverse health outcomes. Like other psychosocial stressors, discrimination is thought to impact health through stress-related physiologic pathways including hypothalamic-pituitary-adrenal (HPA) axis activation, dysregulation of inflammation responses, and accelerated cellular aging. Given growing attention to research examining the biological pathways through which discrimination becomes embodied, this systematic review and meta-analysis synthesizes empirical evidence examining relationships between self-reported discrimination and four biomarker outcomes (i.e., cortisol, C-reactive protein (CRP), interleukin-6 (IL-6), and telomere length) among studies that have used the Everyday Discrimination Scale. We conducted a systematic review of studies discussing self-reported, everyday, or chronic discrimination in the context of health by searching Medline / PubMed (National Library of Medicine, NCBI), PsycInfo (APA, Ebsco) and Web of Science Core Collection (Clarivate). Twenty-five articles met the criteria for meta-analysis, with several reporting on multiple outcomes. Discrimination was associated with elevated CRP levels ( r = 0.11; 95% CI: 0.01, 0.20, k = 10 ), though not cortisol ( r = 0.05 ; 95% CI: −0.06, 0.16, k = 9), IL-6 ( r = 0.05 ; 95% CI: − 0.32, 0.42, k = 5 ), or telomere length ( r = 0.03; 95% CI: − 0.01, 0.07, k = 6 ). We identify several points of consideration for future research including addressing heterogeneity in assessment of biomarker outcomes and the need for longitudinal assessments of relationships between discrimination and biomarker outcomes.

1. Introduction

In addition to the inequitable access to opportunities, resources, and power due to structural oppression at structural, cultural, and institutional levels, discrimination acts as the behavioral expression of oppression, resulting in inequitable treatment for marginalized groups ( Priest et al., 2020a ; Tajfel and Turner, 2004 ). As one of the most frequently assessed domains of discrimination, self-reported discrimination is often conceptualized as a stressor that adversely affects health, with a large proportion of the literature examining the impacts of self-reported racial discrimination ( Dolezsar et al., 2014 ; Gilbert and Zemore, 2016 ; Goosby et al., 2018 ; Krieger, 2014 ; Lewis et al., 2015 , 2014 ; Pascoe and Smart Richman, 2009 ; Williams et al., 2019a , 2019b ; Williams and Mohammed, 2009 ). The study of discrimination as a type of psychosocial stressor that adversely affects health and as a contributor to race/ethnic disparities in health has grown in the last two decades ( Krieger, 2014 ). A recent review documented 29 reviews of the literature that were published between 2013 and 2019 which examined relationships between discrimination and mental and physical health outcomes ( Williams et al., 2019b ). Most early research on the relationship between discrimination and health documented associations with mental health, indicators of health behavior, or self-reported measures of physical health ( Williams and Mohammed, 2009 ). However, research has begun to elucidate the biological pathways through which societal and psychosocial stressors, like discrimination, are embodied to affect health ( Cuevas et al., 2020 ; Priest, 2021 ).

A growing body of evidence suggests that experiences of discrimination may affect physical and mental health through multiple biological pathways ( Clark et al., 1999 ; Epel, 2009 ; Lewis et al., 2015 ). The conceptual model of allostatic load, developed by McEwen and Stellar, suggests that frequent exposure to psychosocial stressors – such as discrimination – results in the activation of multiple axes involved in the stress response, e.g., the neuroendocrine system (HPA axis, sympathetic-adreno-medullar axis), the autonomic nervous system, immune and inflammatory processes, and metabolism ( McEwen, 2000 ; McEwen and Stellar, 1993 ; Seeman et al., 2001 ). As a result of societal processes of social marginalization and devaluing, individuals from marginalized groups experience increased exposure to discrimination which is posited to be embodied through the activation of HPA axis (i.e., cortisol) and inflammation (i.e., interleukin-6 and C-reactive protein) cascades or accelerated cellular aging (i.e., shortened telomere length). These outcomes capture distinct, but inter-related processes through which discrimination may affect health. These systems interact with each other, suggesting a coordinated physiological response to stress. For example, chronic elevation of HPA axis and inflammation mediators result in interactions which yield chronic elevations in blood pressure that can contribute to adverse cardiovascular outcomes such as heart attacks and stroke ( McEwen, 2008 ). Much of the literature supports this framework, with researchers identifying relationships between discrimination and increased HPA axis activation, ( Clark et al., 1999 ) dysregulation of inflammatory responses, ( Cuevas et al., 2020 ) and accelerated cellular aging. ( Epel, 2009 ) Studies have also found biomarkers associated with these pathways (e.g., cortisol, CRP, telomere length) to have documented associations with increased morbidity across several health outcomes and mortality ( Cuevas et al., 2020 ; McEwen, 2008 , 2012 ).

Indeed, closer examination of the relationship between discrimination and biomarkers provides an opportunity to advance our mechanistic understanding of how chronic experiences of differential treatment become embodied or “get under the skin” to contribute to poor psychological and physiological health ( Krieger, 2005 ; McEwen, 2012 ). The use of biomarkers measures also circumvents the issue of common source bias that may arise when both the exposure (discrimination) and health outcome are self-reported. However, a comprehensive assessment of the association between experiences of discrimination and biomarkers of physiologic stress, inflammation, and accelerated aging has not been performed to date.

Studies assessing biological pathways are a small proportion of the total literature on discrimination but are increasing in recent years. A recent systematic review of discrimination and systemic inflammation identified 28 articles published since 2009 ( Cuevas et al., 2020 ). These measures were not included in previous meta-analyses of the health implications of discrimination. Prior meta-analyses have examined the relationship between discrimination and health across several measures of discrimination, with much heterogeneity in the timing and type of discrimination experienced ( Paradies et al., 2015 ; Pascoe and Smart Richman, 2009 ; Pieterse et al., 2012 ). Results from previous meta-analyses suggest that the associations between discrimination and health outcomes vary by instruments used to assess discrimination ( Dolezsar et al., 2014 ; Paradies et al., 2015 ). Reducing heterogeneity in meta-analysis by limiting variations across measures of discrimination, for example, is also important statistically when combining estimates across studies in meta-analysis ( Bourabain and Verhaeghe, 2021 ; Imrey, 2020 ).

The larger literature on stress and health suggests that incidents of racial discrimination, like other self-reported stressors, can be classified into several types of stressful life experiences ( Williams and Mohammed, 2009 ). Similar to research on stress and health, interpersonal experiences of discrimination can be divided into acute major discriminatory life events (e.g. being unfairly fired from a job), chronic discrimination in major domains life (e.g. at work, school, or in one’s neighborhood), traumatic discriminatory experiences (e.g. being beaten by the police) and more minor but ongoing events, somewhat analogous to daily hassles in the larger stress literature ( Williams and Mohammed, 2009 ). Different measures of discrimination assess various aspects of these stressful experiences. The Everyday Discrimination Scale (EDS) captures only the latter class of relatively minor, but recurrent instances of discrimination ( Williams et al., 1997 ). Enhancing our understanding of the ways in which discrimination can affect health requires greater research attention to understanding how specific types of discrimination are related to health outcomes. Social Identity Theory provides a framework that allows for an understanding of how social contexts and identities facilitate differential treatment, devaluing, and withholding of resources by members of the “in-group” can result in the disadvantage that members of marginalized groups face. ( Tajfel and Turner, 2004 ) In this context, marginalized groups are more likely to encounter experiences of discrimination, which have long been theorized to be “assaults to [B]lack dignity and [B]lack hope [that] are incessant and cumulative” in their adverse impacts on health ( Pierce, 1974 ). Understanding the everyday encounters of discrimination marginalized groups face facilitates an understanding of how recurrent exposure to differential treatment becomes embodied and how the EDS remains a strong predictor of the onset and progression of adverse health outcomes ( Kershaw et al., 2016a ; Lewis et al., 2006 ; Williams et al., 2003 ).

A sufficient number of studies have been conducted utilizing the EDS to permit a review of the association of discrimination with biomarkers (i.e., HPA axis, inflammation, and cellular aging). To the authors’ knowledge, this is the first meta-analysis that examines the association of discrimination on stress-related biomarkers among studies that have used the same measure. Accordingly, this paper sought to synthesize existing literature, provide deeper insight into methodological and measurement challenges, and identify future research directions.

1.1. Study objectives

This systematic review and meta-analysis examined the relationship between experiences of discrimination and molecular biomarker outcomes, with quantitative focus on interleukin-6 (IL-6), CRP, cortisol, and leukocyte telomere length, among studies that have used the EDS to measure exposure to discrimination. We characterized the existing body of literature that has included the EDS – highlighting study design and methodology, sample characteristics, operationalization of the EDS, and outcomes examined. We examined relationships between the EDS and individual biomarker measures of stress, inflammation, and cellular aging (e.g., telomere length) – to increase the comparability of findings across studies that have used the same assessment of exposure to discrimination.

Specifically, the overarching research aims of the systematic review were to:

• Meta-analyze associations between the EDS and stress-related biomarkers. We hypothesize that increased discrimination is associated with adverse levels of biomarker measures (i.e., shorter telomere length; higher IL-6, CRP, and cortisol levels).
• Descriptively map the mediators (e.g., smoking, excess drinking) of the associations between discrimination and molecular biomarkers across studies that have used the EDS. This provides context as to what factors have been considered as mediating variables in studies assessing discrimination and biomarker outcomes.

This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines and criteria ( Moher et al., 2009 ; Stroup et al., 2000 ).

2.1. Search strategy

Studies discussing self-reported, everyday, or chronic discrimination in the context of health were identified by searching Medline / PubMed (National Library of Medicine, NCBI), PsycInfo (APA, Ebsco) and Web of Science Core Collection (Clarivate). Controlled vocabulary terms (i.e., MeSH; Thesaurus of Psychological Index Terms) were included when available and appropriate. The search strategies were designed and executed by a research librarian (CM) at the Countway Library of Medicine at Harvard University. Publication date was limited to studies published in 1997 or later. No language restriction was applied. The exact search terms used for each of the databases are provided in the Supplementary materials ( Supplemental Table 1 ). Reference lists of identified papers were examined for additional relevant articles for inclusion.

2.2. Inclusion criteria

For consideration of inclusion, studies must have used quantitative methodology reporting an estimate of the relationship between the EDS and a disease-related molecular biomarker (e.g., telomere length, IL-6) ( Broza et al., 2019 ; Epel, 2009 ; Laterza et al., 2007 ). As such, studies using qualitative methods were not included. All collection methods for molecular biomarkers were included (e.g., blood, saliva, hair, urine) ( Broza et al., 2019 ). All study designs were eligible (i.e., cross-sectional, longitudinal, case-control, and experimental). Given that the EDS was first utilized in 1997, ( Williams et al., 1997 ) studies were eligible for inclusion if published in 1997 or later.

Exclusion restrictions were not placed upon study populations, such that studies including participants from any age group, racial/ethnic/cultural identity, ability, and other sociodemographic factors were included.

2.2.1. Exposure

Self-reported discrimination was measured using the EDS, which includes the frequency of self-reported discrimination in the respondent’s day-to-day life ( Williams et al., 1997 ) The original scale includes nine-items: “In your day-to-day life, how often do any of the following things happen to you?” (1) You are treated with less courtesy than other people are; (2) You are treated with less respect than other people are; (3) You receive poorer service than other people at restaurants or stores; (4) People act as if they think you are not smart; (5) People act as if they are afraid of you; (6) People act as if they think you are dishonest; (7) People act as if they’re better than you are; (8) You are called names or insulted; and (9) You are threatened or harassed. Responses for each item include “almost every day,” “at least once a week,” “a few times a month,” “a few times a year,” less than once a year,” and “never”. Respondents reporting “a few times a year” or more frequent experiences of discrimination may be asked a follow up question: “What do you think is the main reason for these experiences?” Participants can select one or more of the following attributions: (1) your ancestry or national origins; (2) your gender; (3) your race; (4) your age; (5) your religion; (6) your height; (7) your weight; (8) some other aspect of your physical appearance; (9) your sexual orientation; (10) your educational or income level.

A short form of the EDS was developed for the Chicago Community Adult Health Study (CCAHS) ( Sternthal et al., 2011 ) in which respondents were asked: “”In your day-to-day life, how often have any of the following things happened to you?” (1) You are treated with less courtesy or respect than other people; (2) You receive poorer service than other people at restaurants or stores; (3) People act as if they think you are not smart; (4) People act as if they are afraid of you; (5) You are threatened or harassed. The follow-up question and response categories of the shortened EDS are the same as the original. Other adapted versions of the scale were considered eligible for inclusion if they were not major adaptations beyond the original scope of the EDS (e.g., studies that created summary scores that joined the EDS with other measures or studies that only include one item from the EDS were not included).

2.2.2. Outcomes

All stress-related biomarker outcomes were eligible for inclusion. These included IL6, CRP, cortisol, DHEA (dehydroepiandrosterone, also DHEA-S), DNA methylation, E-selectin, fibrinogen, nerve growth factor, alpha amylase, HSP-70 (heat shock protein-70), HbA1c levels, and telomere length.

Several outcomes were only examined in one or two articles and were excluded from the meta-analysis but are included in our narrative synthesis of the findings (N = 5, Fig. 1 ). For example, DNA methylation was only assessed as an outcome in two identified studies. The three remaining outcomes meeting the inclusion criteria were only assessed in one manuscript each.

Study identification and selection process.

2.3. Screening

Search results were imported into Endnote X9, and duplicate entries were removed. The Endnote library was exported into Covidence ( Veritas Health Innovation, 2017 ), a web-based systematic review software. Two reviewers (JL, GM) independently conducted title and abstract screening to assess studies for eligibility (inter-reviewer reliability (κ) = 0.78, indicating good agreement).

Full texts of studies considered for inclusion were obtained. Discrepancies between reviewers regarding study inclusion was resolved by discussion with a third reviewer (HC) and/or consensus (JL, GM) [κ = 0.74]. The study selection process is outlined in full in Fig. 1 .

2.4. Data extraction and analysis

Data from identified studies were independently extracted into an Excel document by one reviewer (JL) with another reviewer randomly checking 20% of the extracted data (HC). Inconsistencies were resolved by consensus and/or discussion with a third reviewer (GM). Extracted data included information regarding the EDS (e.g., version used, operationalization) and biomarker assessed, demographic characteristics of participants (e.g., age, gender, educational attainment), study attributes (e.g., study design, location [country and region], period and duration of study (if relevant), sample size), most and minimally adjusted estimates, covariates adjusted for, psychometric properties of the scale (if assessed), mediators (if explicitly mentioned) and potential sources of bias (e.g., attrition, missing data). For articles using the same dataset to examine relationships with the same outcome, we extracted data from papers with the most information reported (e.g., both minimally and fully adjusted models reported). If multiple papers included the same amount of information, the earliest publication was included in the meta-analysis.

Minimally adjusted estimates include data from the least adjusted model reported or correlations between EDS and biomarkers. Fully adjusted estimates include data from the most adjusted model reported with all covariates included. Efforts were made to contact study authors for additional information; however, if only one estimate was available, it was used as both the minimally and most adjusted estimate.

Most studies reported regression coefficients. To incorporate regression coefficients into the present meta-analysis, we use a derived formula developed by Peterson and Brown to estimate correlation coefficients ( r ) ( Peterson and Brown, 2005 ). After extracting over 1500 β and r values, the authors fit several models to assess the relationships between the two measures. They found that r = 0.98 β + 0.05 λ yielded the best fit, where β is the coefficient reported and λ is an indicator variable that is 0 when β is negative and 1 when β is positive ( Peterson and Brown, 2005 ). After testing this efficacy of this formula against several alternatives, the authors found little difference between results. However, the authors note that this imputation is best used among β estimates within the interval of − 0.50–0.50, given an observed tight joint distribution of β and r values in that range. Given that most estimates from eligible studies were within that range, we imputed r values from reported β values in eligible studies where r values were not reported using r = 0.98 β + 0.05 λ .

Estimates were coded such that greater experiences of discrimination are associated with poorer outcomes (negative for telomere length, positive for inflammation and stress biomarkers (e.g., IL-6)).

Weighted correlation sizes were calculated using large-sample approximation to compute sampling variance ( Viechtbauer, 2010 ). Random effects models were fit utilizing the minimally adjusted associations reported using the “metafor” package ( Viechtbauer, 2010 ) available in R ( R Core Team, 2013 ). Random effect models essentially relax the assumption of fixed-effect models, which assume that there is one ”true” effect estimated in all studies and that variations only occur due to chance (i.e., variations in samples) ( Borenstein et al., 2010 ). Instead, random effects models assume a distribution of correlation sizes allowing for variations in the correlation size across studies, where factors beyond sampling variation may influence the association (e.g., age of sample) ( Borenstein et al., 2010 ). Cochran’s Q test was conducted to test for heterogeneity. Forest plots are presented to illustrate study-specific and overall correlation sizes by outcome and 95% CIs. Sensitivity analyses included estimating the weighted correlation sizes using the most adjusted estimates reported in eligible articles.

2.5. Quality assessment

Study quality was assessed in terms of potential for bias. Similar to Paradies et al. (2015) , we use sampling procedure, data type (e.g., cross-sectional, longitudinal), and instrument (i.e., full scale, short form), and covariates included in a narrative assessment of study quality. Funnel plots were created to illustrate potential publication bias and asymmetry was tested using Egger’s tests ( Egger et al., 1997 ).

Database searches on 03/24/2020 yielded 2803 references, resulting in 1867 unique references for screening. Relevant outcomes were found in 33 articles included in the narrative review and 25 studies were identified for inclusion in the quantitative synthesis of associations in the present study. The number of studies excluded from the quantitative analysis, with reasons, are provided in detail in Fig. 1 . Overall descriptive data for the articles included in the quantitative assessment are summarized in Supplemental Table 2 .

Most studies were published between 2016 and March 2020, with all articles having publication dates between 2010 and 2020. Nearly all articles examined associations among populations in the United States, with one assessing associations among a sample in New Zealand. Nearly 36% of studies implemented representative sampling procedures, with 64% of studies reporting non-representative sampling methods. Many articles reported findings from cross-sectional analyses (72%) with the remainder being longitudinal (24%) or other (4%).

Sample sizes ranged from 49 to 12,624, with a total sample of 37,763 respondents included across all eligible studies. All articles reported some information on participant age (e.g., average age of population), race/ethnicity, and sex; however, two did not report the number of participants within each racial/ethnic group in the analytic samples. Articles were mostly conducted among adults (nearly 99% of the sample size), though populations under 18 were included in three articles, yielding 419 young adult or adolescent participants (< 18 years of age) to the total sample. One study did not report the age range of study participants to discern whether young adults could have been included in the study population. Data on participant educational attainment was reported in 19 studies.

The full version of the EDS was employed in most articles (N = 17), with fewer using the short-form (N = 5) or a modified version of the EDS (N = 3). Attribution of experiences was assessed in 7 studies, with most assessing attributions of experiences to both racial and non-racial reasons (N = 4). The remaining three studies that captured attributions assessed only racial or non-racial attributions. Operationalization of the EDS remained consistent across studies with most measuring experiences as the sum (N = 11) or the average (N = 10) of the frequency of experiences. Other means of operationalizing the EDS included a count of yes responses to experiences, dichotomizing beyond a certain threshold. How the measure was operationalized was unclear in one analysis. Among studies that examined the reliability of the EDS, it exhibited very good reliability using a Cronbach’s alpha cutoff of greater than 0.80 in 18 of the 25 articles.

Cortisol and CRP were the most frequently assessed biomarker outcomes (N = 9 and N = 10, respectively), followed by telomere length (N = 6) and IL-6 (N = 5). Approximately 16% (N = 4) of articles reported associations between the EDS and multiple biomarker outcomes.

Supplemental Table 3 presents the summary of study and sample characteristics by outcome. Weighted correlation sizes from the most adjusted associations reported between the EDS and each biomarker outcome are presented in Figs. 2 , ​ ,3, 3 , ​ ,4, 4 , and ​ and5 5 .

Associations between EDS and (a) all cortisol outcomes; (b) cortisol awakening response (CAR); and (c) waking levels (minimally adjusted).

Associations between EDS and C-reactive protein (CRP, minimally adjusted).

Associations between EDS and (a) interleukin-6 (IL-6) across all studies; (b) plasma samples; and (c) salivary samples (minimally adjusted).

Associations between EDS and telomere length (minimally adjusted).

3.1. Cortisol

Nine studies examined relationships between discrimination and cortisol. Most frequently, the EDS was operationalized as the mean (N = 4) or sum of frequencies (N = 3). Another study used the count of yes responses, though one study did not clearly specify how the measure was operationalized. Studies were primarily cross-sectional (N = 7) and conducted among adults (N = 6). Black participants comprised nearly 29.4% of the cortisol study population, followed by Latinx/Hispanic (18.6%) and Asian (7.7%) participants; however, white participants (38.6%) comprised the largest proportion of the study population across all 9 studies. Native Hawaiian, Pacific Islander, or Māori, multiracial, and individuals categorized as “other” racial groups together comprised the remaining 5.7% of the pooled study population.

Assessments of cortisol varied across studies. Given the evidence of changes in cortisol levels throughout the day, ( Levine et al., 2007 ; Weitzman et al., 1971 ) some studies assessed salivary cortisol by collecting multiple samples per day at different time points (≥4) over several days (≥3) ( Doane and Zeiders, 2014 ; Fuller-Rowell et al., 2012 ; Huynh et al., 2016 ; Zeiders et al., 2014 ). Others collected two saliva samples (morning and evening) over two consecutive days Thayer and Kuzawa, 2015 ), three salivary samples in one day, ( Incollingo Rodriguez et al., 2019 ) salivary samples before, during and after exposure to a stress task ( Lucas et al., 2017 ) and the average of duplicate samples collected in one afternoon ( Ratner et al., 2013 ). Another study assessed cortisol concentration through hair cortisol, using 3 cm of hair closest to the scalp to assess retrospective cortisol levels ( Lehrer et al., 2020 ). In the main analysis, the reported assessment of cortisol levels varied, with articles assessing associations between the EDS and waking cortisol levels in five studies, baseline cortisol, average cortisol from one measurement, total daily cortisol, and hair cortisol concentration. Five studies reported both minimally and fully adjusted estimates, while the remaining reported only unadjusted (N = 2) or adjusted (N = 2).

The mean correlation coefficient for associations between EDS and cortisol was r = 0.05 [95% CI: − 0.06, 0.16, k = 9; Q= 19.83, df= 8, p = 0.011] ( Fig. 2A ), suggesting no observed association with cortisol levels. Patterning in the direction of responses was observed, where larger studies showed null or negative associations while smaller studies typically had associations indicating greater cortisol levels with increased discrimination. Minimally adjusted models included four correlations and models that accounted for factors including age, race, sex or gender, BMI, socioeconomic indicators (i.e., household income, educational attainment, material deprivation), health behaviors (i.e., exercise, food, alcohol and caffeine consumption, cigarette use), daytime sleep, daily wake and sleep time, psychological factors (i.e., stress level, emotional stability), and medication (i.e., cortisol medication, other medication use) or medical history (i.e., C-section delivery).

Several studies reported estimates between the EDS and cortisol outcomes using the same measure (i.e., cortisol awakening response [CAR], waking levels). To minimize the impact of heterogeneity in the measurement of cortisol on the pooled estimate, we estimated mean correlation sizes for studies that examined the CAR ( Doane and Zeiders, 2014 ; Fuller-Rowell et al., 2012 ; Huynh et al., 2016 ; Incollingo Rodriguez et al., 2019 ; Zeiders et al., 2014 ) (defined as the change in cortisol from waking to a defined time period after waking) and waking cortisol levels ( Doane and Zeiders, 2014 ; Fuller-Rowell et al., 2012 ; Huynh et al., 2016 ; Thayer and Kuzawa, 2015 ; Zeiders et al., 2014 ). Among studies that evaluated the relationship between the EDS and waking cortisol, the mean correlation size was r = 0.01 ( Fig. 2B , 95% CI: − 0.18, 0.19). Whereas the mean correlation size among studies reporting associations between the EDS and CAR was r = 0.00 ( Fig. 2B , 95% CI: − 0.22, 0.22 ). These findings suggest that discrimination is not associated with cortisol levels, specifically waking and the cortisol awakening response.

Sensitivity analyses were conducted using the most or fully adjusted estimates reported in each study. The mean correlation size did not differ greatly across fully adjusted estimates ( r = 0.06; 95% CI: − 0.06, 0.18) compared to the minimally adjusted models. Associations between discrimination and CAR ( r = 0.02; 95% CI: − 0.24, 0.29 ) and waking cortisol ( r = 0.00; 95% CI: − 0.19, 0.18 ) remained null. Beyond covariates included in the minimally adjusted models, fully adjusted models also included factors such as psychological factors (i.e., neuroticism risk, public and private esteem), average hours of sleep, medication (i.e., contraceptive use), waist-to-hip ratio, and attributions of discrimination.

3.2. C-reactive protein (CRP)

Among the ten eligible studies assessing the association between discrimination and CRP, the EDS was frequently implemented as the sum (N = 5) or mean (N = 4) of the frequencies of experiences of discrimination. One study operationalized the EDS as the sum of the experiences ( Lewis et al., 2010 ). Nine of the ten studies reported the racial/ethnic composition of the analytic samples, with 38% identifying as Black, 7% as Latinx/Hispanic, 2% as Asian and 52% as white/European. A small percentage of participants were classified as “Other” race (1%). Most studies were cross-sectional in design (50%) and conducted among adult populations (N = 9). CRP was assessed consistently, with most studies using blood/serum levels of CRP (N = 9) and one using a measure of salivary CRP levels.

The pooled correlation size for the associations between discrimination and CRP was r = 0.11 [95% CI: 0.01, 0.20; k = 10; Q = 69.90, df= 9, p < 0.001 ] . Correlation sizes appear to be larger in smaller studies, though larger studies also show relationships between discrimination and CRP. Minimally adjusted estimates included one unadjusted correlation and models which accounted for factors such as age, race/ethnicity, lifetime experiences of discrimination, measures of socioeconomic status (e.g., income, educational attainment, employment status), BMI and medications (e.g., statin use, hormone replacement therapy, anti-inflammatory use). Three articles did not report unadjusted associations, ( Beatty Moody et al., 2014 ; Saban et al., 2018 ; Zahodne et al., 2019 ) though one only accounted for age, BMI and statin use in the adjusted estimate reported ( Saban et al., 2018 ).

Supplemental Fig. 2 illustrates the reported associations and mean correlation size using the most adjusted estimates reported. Significant associations were observed [ r = 0.09; 95% CI: 0.01, 0.17, k = 10]. These associations remain considering the covariates included in the most adjusted models reporting these associations. One paper only reported a minimally adjusted association (correlation), however other articles accounted for factors such as race, age, sex, BMI, measures of socioeconomic status (e.g., financial strain, educational attainment, income), psychological factors (e.g., depressive symptoms, cynicism) and lifetime experiences of discrimination, health behaviors (e.g., physical activity, smoking, alcohol consumption), measures of physiological functioning (e.g., blood pressure, cholesterol and triglyceride levels, HbA1c, vital capacity, adiponectin), health conditions (e.g., heart attack, other vascular diseases, diabetes), and medications (e.g., statin use, anti-hypertensives, diabetes management medications). The similarities in mean correlation sizes from the most and minimally adjusted estimates reported suggest that the relationship between discrimination and CRP is robust to covariate adjustment and may not be strongly mediated by health behaviors (e.g., smoking, drinking).

3.3. Interleukin-6 (IL-6)

Among the 5 studies examining IL-6, the EDS was operationalized as the sum of frequencies (N = 3) or mean of frequencies (N = 2). White/European participants comprised over 80% of the sample across studies reporting data on race/ethnicity (N = 4). Measurement of IL-6 levels was captured through blood (N = 3) or saliva (N = 3). One study assessed both blood and salivary IL-6 levels, though only the adjusted association was reported for the blood IL-6 outcomes ( Saban et al., 2018 ). Eligible studies used in the meta-analysis were all cross-sectional in design and conducted among adult populations.

The mean weighted correlation size between discrimination and IL-6 suggests discrimination may not be correlated with elevated IL-6 levels ( r = 0.05 ; 95% CI: −0.32, 0.42, k = 5; Q = 47.01, df = 4, p < 0.001). Minimally adjusted estimates included an unadjusted correlation (N = 1) and models (N = 4) that accounted for factors such as race/ethnicity, gender, age, measures of socioeconomic status (i.e., income, educational attainment, employment status), medication use (i.e., anti-inflammatory, hormone replacement therapy), and time. Larger correlation sizes were observed among two smaller studies; while one association went in the opposite direction, indicating an inverse relationship between discrimination and IL-6 levels.

Additionally, when assessed by measurement of IL-6 (i.e., plasma, salivary), we find the direction of the mean correlation size for the minimally adjusted estimates to be similar among both measures ( r = 0.03; 95% CI: − 0.03, 0.09 and r = 0.06; 95% CI: − 0.99, 1.12 for plasma and salivary measures, respectively). However, the confidence interval is larger among studies using salivary measures of IL-6, possibly indicating greater variability in estimates derived from salivary samples. These assessments should be interpreted with caution given the small sample size for these assessments (k = 3 for each) and that one study reported only fully adjusted associations between discrimination and plasma IL-6 levels.

Supplemental analysis of the most adjusted estimates reported resulted in a stronger correlation between increased experiences of discrimination and IL-6 levels [ r = 0.07; 95% CI: − 0.28, 0.42, k = 5], however, the confidence interval is wide and crosses the null. Examining the forest and tree plot, we observed null associations in studies of varying sample sizes (two, relatively large and one small), though the remaining two studies find lower and elevated IL-6 levels to be associated with increased discrimination. The observed null associations may be a function of covariates included in each model. In most adjusted models, several studies accounted for what could be potential mediators or moderators of the relationship between discrimination and IL-6 levels. Covariates included age, race, marital status, measures of socioeconomic status (i.e., income, employment status, educational attainment), psychological factors (i.e., measures of depression, anxiety, reactivity), perceived social status, reported childhood trauma, medication use (i.e., cholesterol, blood pressure, diabetes, hormone replacement), public and private esteem, BMI, and alcohol consumption.

One study explicitly assessed BMI as a potential mediator of the relationship between discrimination and IL-6 in a sample of men and women ( Kershaw et al., 2016b ). Among women, the authors found the positive relationship between everyday discrimination and IL-6 to be attenuated by BMI. However, the inability to establish temporality given the cross-sectional analysis does not provide insight as to whether BMI is subsequent to exposures to discrimination or whether it may increase experiences of discrimination ( Kershaw et al., 2016b ).

3.4. Telomere length

Three of the six eligible studies operationalized the EDS as the sum of reported frequency of discrimination. Assessments also included the mean of frequency of experiences of discrimination (N = 2) and a dichotomized assessment of if a respondent ever experienced everyday discrimination and attributed it to a personal characteristic (yes/no). The racial/ethnic breakdown of analytic samples were provided in 5 of the 6 studies, with white participants comprising 60% of the overall study populations. Black participants comprised approximately 33% of the overall sample size, followed by Latinx/Hispanic participants (7.4%). Asian, Native Hawaiian/Pacific Island, multiracial or “Other” racial/ethnic individuals were not represented in the studies eligible for inclusion. All eligible studies used quantitative polymerase chain reaction (qPCR) to assess and quantify telomere length, which is optimal for large studies given the small sample needed to replicate DNA and assess telomere length ( Montpetit et al., 2014 ). Additionally, all studies utilized leukocyte samples to ascertain telomere length. Three studies examined associations between discrimination and telomere length using the ratio of telomeric length of DNA to a single-copy control gene (T/S ratio) which is correlated with telomere length, ( Hailu et al., 2020 ; Liu and Kawachi, 2017 ; Lu et al., 2019 ) while others converted the T/S ratio to kilobase or base pairs to compare differences in length ( Beatty Moody et al., 2019 ; Geronimus et al., 2015 ; Sullivan et al., 2019 ).

Everyday discrimination was not associated with telomere length when minimally adjusted models were assessed ( r = 0.03; 95% CI: − 0.01, 0.07, k = 6; Q =7.26, df = 5, p = 0.202 ) . Examining the forest and tree plot, we observe that most studies indicate a null association, with larger studies finding discrimination to be associated with longer telomere length. Minimally adjusted estimates included unadjusted regression coefficients (N = 2), estimates from an age-adjusted model (N = 1), and two adjusted estimates that accounted for age, race, sex, measures of socioeconomic status (i.e., poverty-to-income ratio; educational attainment); and psychosocial stress (i.e., safety stress, physical environment, and negative social interactions).

Supplemental analyses of fully adjusted estimates exhibited similar associations. The mean correlation size using the most adjusted estimates reported were not statistically significant [r = 0.02; 95% CI: − 0.02; 0.06]. Models accounted for factors such as age, race, sex, measures of socioeconomic status (i.e., poverty-to-income ratio; educational attainment); and psychosocial stress (i.e., safety stress, physical environment, negative social interactions, perceived stress); psychological factors (i.e., depression, reaction type); smoking status; BMI; health conditions (e.g., diabetes, hypertension, myocardial infarction, cancer), Census region of birth; childhood health; lifetime substance use and physical activity.

Two studies explicitly examined potential mediators of the relationship between discrimination and telomere length. Work by Liu and Kawachi assessed whether physical activity, smoking status, and having a BMI ≥ 30 kg/m 2 mediated the relationship between discrimination and telomere length ( Liu and Kawachi, 2017 ). The authors found evidence that suggested these factors mediate the relationship between everyday discrimination and telomere length, observing attenuated associations when these factors were included in regression analyses. Sullivan et al. examined whether depressive symptoms and perceived stress mediated the relationship between discrimination and telomere length ( Sullivan et al., 2019 ). The authors found that observed associations between everyday discrimination and telomere length among Black and white women remained after accounting for mediating variables, with correlation sizes remaining larger (i.e., shorter telomere length) for Black women; though no associations were observed among men.

Across three of the four analyzed outcomes, between study heterogeneity was high and statistically significant as measured by the Cochran’s Q test. Results from the Q-test reject the null hypothesis of the “true” effect being the same across studies and only differing due to sampling variability, indicating that other factors may influence biomarker outcomes.

3.5. Quality assessment

The limited availability of longitudinal assessments of the relationship between the EDS and biomarker outcomes leaves us unable to assess the temporality of associations. Across all outcomes, most studies were cross-sectional (77.8%, 50%, 100%, and 100% for cortisol, CRP, IL-6, and telomere length respectively). Several studies utilized nonrepresentative sampling procedures (N = 7, 7, 3, and 1 for cortisol, CRP, IL-6, and telomere length, respectively). This may raise concerns regarding potential bias such that correlation sizes may be estimated from samples that may not be generalizable, however they do provide context to the experiences of individuals from similar backgrounds (i.e., communities with similar sociodemographic characteristics). However, most studies assessing representative samples contributed greater weights to the estimated mean correlation size given the small variances across all outcomes. Most studies used the full EDS or short form (N = 8, 10, 4, 4), with few utilizing modified versions. Among studies reporting the Cronbach’s alpha (N = 22), α was greater than or equal to 0.70 suggesting acceptable or better internal consistency of the measure. Studies reporting adjusted models accounted for several socioeconomic, demographic, and health-related covariates that may confound the relationship between discrimination and biomarker outcomes. Adjusted models sometimes accounted for potential mediators of the relationship (i.e., perceived stress) that may have partially accounted for the effect of discrimination.

3.6. Assessment of publication bias

Funnel plots ( Fig. 6 ) and Egger’s tests were used to evaluate the possibility of publication bias. Among studies that examined cortisol, eligible studies tended to have smaller standard errors, but eligible studies had positive, negative, and null associations. Results from the Egger’s test to assess funnel plot asymmetry in funnel plots were not statistically significant (t = 1.91, df = 7, p = 0.098), suggesting that the funnel plot for cortisol is not imbalanced (i.e., no publication bias). Assessment of the funnel plot for CRP outcomes appears to be asymmetric. Eligible studies tend to have small standard errors or larger correlation sizes. Results from the Egger’s test were statistically significant (t = 4.57, df = 8, p = 0.002), suggesting potential publication bias. Fewer studies examined IL-6 and telomere length. The funnel plot for IL-6 appears to be relatively symmetric, with eligible studies having variations in correlation size and standard error. One study was included that documented associations in the opposite direction for IL-6 (i.e., lower IL-6 levels for increased report of discrimination). Eligible studies examining telomere length had varying directions (i.e., null, and positive associations reported). The Egger’s test for IL-6 was not statistically significant, suggesting that publication bias may not be a concern (t = 0.30, df = 3, p = 0.785); however, Egger’s test for telomere length was significant (t = −3.00, df = 4, p = 0.040) indicating the possibility of publication bias. These results should be interpreted with caution as the Egger’s test has limited power when used in a small sample of studies.

Funnel plots for A) cortisol; B) CRP; C) IL-6; and D) telomere length.

3.7. Narrative review

Five studies that were relevant to our review but were not included in the meta-analysis are narratively synthesized here. Friedman et al. found that everyday discrimination was associated with greater E-selectin levels, an indicator of inflammation response, among men, but not women in a sample of adults in the Midlife in the United States study (MIDUS) ( Friedman et al., 2009 ). Using data from a community sample of adults with poorly controlled type 2 diabetes, Potter et al. found that everyday discrimination attributed to weight was associated with elevated HbA1c levels ( Potter et al., 2015 ). Relationships between everyday discrimination and DNA methylation, an indicator of stress, were assessed in two studies ( Santos et al., 2018 ; van der Laan et al., 2020 ). Among a sample of Latina mothers, Santos et al. found that everyday discrimination was inversely associated with DNA methylation (less methylation with increased discrimination), ( Santos et al., 2018 ) while van der Laan et al. found everyday discrimination to be positively associated with DNA methylation among participants in the Research on Obesity and Diabetes among African Migrants (RODAM) study ( van der Laan et al., 2020 ) Saban and colleagues examined the relationship between several social factors – including everyday discrimination – and heat shock protein-70 (HSP-70), another stress-related biomarker, in a small sample of Black and white women with atherosclerosis ( Saban et al., 2014 ) The authors did not observe an association between discrimination and HSP-70 levels, though this association should be examined in a larger study population.

4. Discussion

Though previous meta-analyses have examined the relationship between discrimination and several health outcomes, variations in the measurement of discrimination have made cross-study comparisons difficult. Evidence from the most recent meta-analysis suggests that the relationship between discrimination and health outcomes vary according to the measure of discrimination used ( Paradies et al., 2015 ). This current systematic review and meta-analysis is the first to standardize the measure of discrimination to assess the association of discrimination and health by restricting the analysis to studies that have used the Everyday Discrimination Scale. These findings also contribute to the literature by estimating the pooled correlation coefficient across studies that have examined the relationship between discrimination and molecular biomarkers of stress, inflammation, and cellular aging.

We found that most eligible studies operationalized the EDS as the mean or sum of reported frequency (N = 21 of 25). Our findings also suggest that increased self-report of discrimination is associated with higher CRP levels, though we did not observe evidence of associations between discrimination and cortisol, IL-6, or telomere length when using the EDS. We also observed patterns in the magnitude of associations by sample size. For example, larger positive correlation sizes were observed among two smaller studies examining associations between the EDS and IL-6 while more modest positive correlations were observed among two larger studies. One small study had a large negative association, which may have influenced the null finding for IL-6.

Null associations between discrimination and cortisol and telomere measures were not surprising as neither of these biomarkers have been consistently associated with other types of stress ( Chida and Steptoe, 2009 ; Fogelman and Canli, 2018 ; Korous et al., 2017 ; Mathur et al., 2016 ). However, we identified associations between discrimination and CRP, consistent with associations observed in other systematic reviews and meta-analyses of self-report measures and stress tasks ( Cuevas et al., 2020 ; Steptoe et al., 2007 ). The correlation of discrimination with higher CRP, but not IL-6 suggests that more studies are needed on the latter, given that IL-6 stimulates the production of CRP ( Papanicolaou et al., 1998 ). This may also reflect a need to examine alternative measures of cumulative and chronic inflammation, such as glycoprotein acetyls (GlycA), instead of acute phase inflammatory markers ( Priest, 2021 ). However, studies have identified both IL-6 and CRP to have independent relationships with several adverse health outcomes and risk factors ( Bermudez et al., 2002 ; Pradhan et al., 2001 ).

We noted three factors (1) heterogeneity in outcome measurement; (2) study design; and (3) sample demographics that could have contributed to our mixed findings. First, the observed findings between discrimination and cortisol, IL-6, and telomere length may be influenced by several factors related to outcome measurement. Specifically, eligible studies differed in their operationalization of biomarker outcomes. Among studies that examined cortisol, differences in both the number of samples captured and cortisol outcomes assessed (e.g., momentary cortisol, hair cortisol concentration) were observed. For example, heterogeneity may be introduced by including hair cortisol in this analysis given that hair samples capture cortisol levels over a period ranging from several weeks to months ( Iob and Steptoe, 2019 ). Additionally, cortisol levels fluctuate throughout the day, typically with higher levels at waking and lower during the evening ( Levine et al., 2007 ; Weitzman et al., 1971 ) and are sensitive to the method of collection (i.e., blood, saliva) ( Levine et al., 2007 ). Collecting sufficient data to understand individual cortisol fluctuations and utilizing measures of diurnal cortisol may be useful contributions to future research ( Adam et al., 2017 ).

We also observed differences in how inflammation was assessed among eligible studies assessing IL-6. IL-6 samples were collected through blood (N = 3) or saliva (N = 3), with one study assessing both. While the mean correlation size across studies that used either measure was similar ( r = 0.03; r = 0.06 , plasma and saliva respectively), we observed a wider confidence interval across studies using salivary assessments. This could reflect greater variability in salivary assessments of IL-6; however, the intervals may also be wide given the limited number of studies available. These differences suggest consideration of the means of assessment of inflammatory markers. This is especially relevant given that salivary assessments of inflammation may capture oral rather than systemic inflammation ( Priest et al., 2020b ). Previous research has concluded that plasma and salivary samples of inflammatory biomarkers (i.e., IL-6, CRP) may not be strongly correlated, and that blood samples – though relatively invasive – are preferred to salivary measures to assess systemic inflammation ( Cullen et al., 2015 ; Williamson et al., 2012 ).

Optimal assessments of telomere length are still being explored. All eligible studies used qPCR to assess telomere length which has several strengths that have been summarized in detail elsewhere ( Montpetit et al., 2014 ). These strengths include that qPCR requires a small sample of DNA, is easily implemented in large studies, and has a reference to compare samples to. However, this method is sensitive to the quality of the DNA sample and the reference is not standardized which makes cross-study comparisons difficult ( Montpetit et al., 2014 ). Additionally, qPCR provides an estimate of the telomere amplification product (T) as compared to that of a reference single-copy gene (S) ( Aviv et al., 2011 ; Montpetit et al., 2014 ). This is used to create a T/S ratio that correlates with average telomere length, but does not yield a base pair estimate ( Montpetit et al., 2014 ). While qPCR has been widely accepted as an approach to assess telomere length, other techniques exist to determine telomere length ( Montpetit et al., 2014 ). These include flow-fluorescence in situ hybridization (FISH) and Southern blot, which is often referred to as the golden standard ( Aviv et al., 2011 ). The FISH method is labor intensive and is likely not useful for large scale epidemiologic studies, as obtaining needed samples can be difficult compared to qPCR and Southern blot ( Aviv et al., 2011 ; Montpetit et al., 2014 ). However, both qPCR and Southern blot yield reproducible results though the measurement error is greater in qPCR analyses ( Aviv et al., 2011 ). Both qPCR and Southern blot come with a set of tradeoffs that should be explored regarding their ability to impact cross-study comparisons. Across all biomarkers used, differences in sample types and quality, frequency of measurement, as well as methodology used highlight a need to identify “gold standard” measures of biomarker levels and implement consistency in biomarker operationalization across studies.

Second, study design may have influenced our findings. Several studies employed non-representative sampling. This may reflect populations that are more or less likely to report experiences of discrimination and are willing to have their biomarkers sampled (e.g., have a blood draw) which is likely to introduce further selection bias. While these findings may not be generalizable to a broader population, they still provide insight into the experiences of individuals and communities with similar characteristics. The preponderance of cross-sectional studies in our review limits the ability to establish a temporal order between exposure to discrimination and biomarker changes, although the use of biomarkers reduces the possibility of reverse causality (i.e., people are generally unaware of their levels of circulating inflammatory biomarkers and hence biomarkers are unlikely to influence reports of discrimination). The longitudinal assessment of experiences of discrimination also makes it possible to examine trajectories of experiences over time and the cumulative impacts of discrimination on biomarker outcomes. Priorities for future research on discrimination and health include the need for more longitudinal assessments and representative sampling, particularly of marginalized groups that may be most susceptible to experiencing discrimination and the differential health, social, and economic burden of such experiences.

Though not quantifiable in the present analysis given the limited number of studies, findings from individual studies suggest there may be heterogeneity in the associations of discrimination and biomarkers according to race/ethnicity, gender, and/or sexual orientation ( Doyle and Molix, 2016 ; Kershaw et al., 2016b ; Ratner et al., 2013 ; Saban et al., 2018 ). Specifically, studies that examined associations among marginalized groups observed more nuanced associations than the average correlation obtained from our pooled estimates. For example, Lehrer et al. found that everyday discrimination was associated with hair cortisol concentration among Black participants, though not white participants ( Lehrer et al., 2020 ). Differential relationships among marginalized groups may be obscured in assessments where their experiences are not centered or when included in study populations where those groups are less represented. These relationships should be explored in future research. Additionally, while the literature on discrimination and health is global and spans across the lifecourse, ( Paradies, 2006 ; Williams et al., 2019b ) assessments of the association between discrimination and biomarkers that use the EDS have been predominantly carried out in the United States (N = 24) and among adult populations. Associations between discrimination and biomarkers should be examined in other national contexts and lifecourse periods to assess comparability. While the underlying mechanisms may not differ, cross-context studies can help to elucidate causal mechanisms and effect modifiers useful to understanding relationships between discrimination and health. Associations between discrimination and biomarkers across the lifecourse may vary at different periods (i.e., early life, adolescence, mid-life, older age). Relatedly, items in the EDS may not perform the same in different countries and populations. For example, to extend the use of the measure, the EDS has recently been adapted for Aboriginal and Torres Strait Islander peoples to capture experiences and attributions relevant to Indigeneity in Australia ( Thurber et al., 2021 ).

It should also be noted that there is a body of literature that suggests that psychosocial factors and coping strategies may influence reports and impacts of experiences of discrimination ( Berjot and Gillet, 2011 ; Brondolo et al., 2009 ; Pascoe and Smart Richman, 2009 ). Unhealthy coping strategies (e.g., suppressing responses) or “negative” psychosocial factors have maintained fairly consistent associations with worsened health impacts of discrimination, ( Brondolo et al., 2009 ; Himmelstein et al., 2015 ; Krieger and Sidney, 1996 ; Nuru-Jeter et al., 2009 ) though findings regarding the overall health impacts of active or health-promoting coping strategies and positive psychosocial factors have been mixed ( Brondolo et al., 2009 ; James, 1994 ; Pascoe and Smart Richman, 2009 ). While insight into how individual-level coping behaviors and resources is useful and can inform individual-level interventions, the focus should extend beyond the individual-level. Though these measures may be useful in understanding how marginalized people respond to and cope with discrimination – and should be further explored – focus on individual coping strategies without intervening on the structural and cultural factors that pattern these experiences may do little to mitigate adverse health outcomes and sustain wellbeing ( Bailey et al., 2020 , 2017 ; Homan, 2019 ; James, 1994 ).

Our search found that CRP, cortisol, IL-6, and telomere length were outcomes that were assessed with reasonable frequency. However, in addition to studies in the meta-analysis, the narrative review revealed a broader range of biomarkers for future research. In addition to those identified in our narrative synthesis of relevant papers, several inflammatory markers such as IL-1β, TNF-α, and inflammatory mechanisms such as the Conserved Transcriptional Response to Adversity (CTRA) have been identified in a recent review of the relationship between discrimination and inflammation ( Cuevas et al., 2020 ). Measures such as GlycA may be more accurate measures of cumulative inflammation and have been associated with several chronic and acute health outcomes in adults ( Priest, 2021 ). This literature suggests that future research should examine relationships between discrimination and these understudied indicators of biological functioning to better understand and intervene upon the health implications of societal conditions and contexts.

While future research should increase focus on relationships between discrimination and biomarkers (as well as potential interventions), it should also consider whether these associations differ when other measures of discrimination are used (e.g., Experiences of Discrimination, ( Krieger, 1990 ; Krieger and Sidney, 1996 ) Major Experiences of Discrimination Scale, ( Williams et al., 1997 ) or Schedule of Racist Events (SRE) ( Landrine and Klonoff, 1996 )). Studies using the Experiences of Discrimination (EOD) scale have found positive associations with IL-6 levels ( Giurgescu et al., 2016 ). A cross-sectional analysis by Chae and colleagues found the main effect of EOD on telomere length to be null in a sample of Black men ( Chae et al., 2014 ), though, in a recent longitudinal assessment, they found evidence of greater 10-year telomere shortening among a sample of Black adults in the CARDIA study ( Chae et al., 2020 ). Differences in findings of these assessments may reflect differences in 1) study design (i.e., cross-sectional vs. longitudinal) or 2) study population (e.g., middle-aged Black men in the Bay Area vs. a broader sample of middle-aged Black adults across 4 cities). Research examining the relationship between discrimination using the SRE and cytokine levels (i.e., indicators of inflammation) found increased discrimination to be associated with elevated cytokines ( Brody et al., 2015 ; Simons et al., 2021 ). An eligible study also assessed associations between discrimination and telomere length using the Major Experiences of Discrimination Scale finding null associations ( Hailu et al., 2020 ). It seems that the direction of relationships is relatively consistent across measures, though these are comparisons to individual studies. However, each measure captures different domains and frequencies in which discrimination occurs and offers considerations of different policy suggestions and interventions to mitigate their impacts. Future research estimating the pooled correlation size between various indicators of discrimination and biomarker outcomes across studies that use measures that capture different forms, severity, and specific attributions of discrimination could contribute to understanding how discrimination adversely affects indicators of health status across the continuum of disease. It may also be useful to understand whether these associations differ among measures of discrimination that do not rely on the willingness of an individual to report compared to those which require self-report ( Krieger et al., 2010 ). Additionally, this work can contribute to an evidence base that emphasizes the importance of policy and programs in tandem with research to intervene prior to the development of diseases to prevent and reduce disease burden among marginalized groups.

The present meta-analysis is not without its limitations. In examining the relationship between discrimination and biomarkers using the EDS, we rely on a measure of discrimination that captures general experiences of unfair and differential treatment. While useful, the EDS is distinct from measures that capture discrimination occurring within institutional contexts (e.g., the EOD scale), those that capture specific forms of oppression (e.g., the SRE), measures that capture discrimination as a result of an individual’s multiple marginalized identities (e.g., Multiple Discrimination Scale ( Bogart et al., 2010 )), and those capturing experiences which result in material, opportunity, and political deprivation irrespective of whether an individual was aware of such experiences and reported them as discriminatory or harmful ( Bailey et al., 2017 ; Krieger, 2011 , 2012 ; Williams and Mohammed, 2009 ). Additionally, we only include findings from published manuscripts, which may differ from associations reported in unpublished works. Specifically, results from the Egger’s test suggests publication bias among studies that assessed CRP, though not for cortisol, telomere length, or IL-6. This may reflect a trend of not publishing null findings for CRP and may also reflect the need for more research on IL-6, telomere length, and cortisol given the smaller number of studies identified. We estimated mean correlation sizes from minimally adjusted associations reported in each article, however we also examine associations reported in most adjusted models to account for potential confounders of the association.

The biomarkers included in our review are linked with each other, resulting in a cascade of physiological responses to stress. For example, chronic inflammation (measured through IL-1β and IL-6) may lead to shortened telomeres, ( Baylis et al., 2014 ) acting as a potential mediator between discrimination and telomere length. However, our review did not consider the complex, inter-relationships between biomarkers representing different systems. Instead, we have focused on summarizing the associations with individual components of the stress response, as well as reveal gaps in the evidence.

This study also has several strengths. It quantifies the relationship between discrimination and molecular biomarkers, which provide evidence for some of the pathways that discrimination may become embodied. We also examine the relationship among studies that use the same measure of discrimination, the EDS, thus increasing the comparability across studies. The EDS is a widely used measure in both domestic and international contexts. Full, abbreviated, or modified versions of the EDS are included in many major epidemiologic studies in the United States and elsewhere (See for example: ( Bild et al., 2002 ; Heeringa and Connor, 1995 ; Jackson et al., 2004 ; Radler, 2014 ; Rosenberg et al., 1995 ; Steptoe et al., 2013 ; Taylor et al., 2005 ; Williams et al., 2004 )). The frequent inclusion of EDS in cross-national studies to examine the implications of discrimination on health allows for the systematic examination of the strength of associations between discrimination and health using a standardized exposure. Additionally, the utility of the EDS in capturing, reasonably accurately, the experiences of discrimination has been documented across a wide range of populations, with good internal consistency and validity ( Gonzales et al., 2016 ; Kim et al., 2014 ; Krieger et al., 2005 ; Lewis et al., 2012 ). We also evaluate, where possible, the relationship between discrimination and biomarkers among studies that have utilized similar means of outcome assessment (i.e., CAR, waking cortisol, blood, and salivary IL-6) to further increase the comparability across studies.

Overall, our results provide information on the relationships between discrimination and several molecular biomarkers. The number of studies was limited, but we did find associations consistent with discrimination having an adverse effect, though evidence can be strengthened. There is a need of research using a broader range of biomarkers to better characterize the relationships between discrimination and physiological indicators. This study identifies associations between discrimination and biological indicators that have been identified as possible precursors to adverse health outcomes using a consistent measure of discrimination. We also provide considerations for future research utilizing biomarker outcomes to strengthen ongoing efforts.

Supplementary Material

Supplemental tables and figures.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi: 10.1016/j.psyneuen.2022.105772 .

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Essay on Social Class Inequality & Discrimination

Need to write a social class inequality essay? Discrimination and injustice might take place everywhere: in the spheres of education, healthcare, and so on. Find here critical reviews of three articles on the topic. Get inspired to write your own story of social class and inequality!

Introduction

• The War Against the Poor
• Middle of the Class
• When Shelter Feels Like a Prison

Works Cited

There are several attitudes that the middle class and the rich have towards the poor. These attitudes stem from the belief that the world is a just place and people get what they deserve. If one works hard enough and perseveres he or she will be rich. However, the poor person is in that state because of poor decisions such as immorality, crime and alcoholism, lack of ambition and perseverance.

These negative attitudes have caused the middle class and the rich to distance themselves from the poor. The stereotyping of the poor is the genesis of class discrimination. The poor have been excluded as the rest of the nation goes on with their lives.

In this paper, I analyze three articles on social class and inequality to find out whether the authors’ views agree with mine on the negative attitudes towards the poor by the middle class and the rich and the way they have distanced themselves from the poor.

Social Inequality in The War Against the Poor

Herbert Hans, in his article the war against the poor instead of programs to end poverty is arguing that government officials are not addressing poverty but instead making life difficult for the poor. Welfare expenses have always been small however the budget is becoming more and more restrictive.

The poor are being accused of enjoying welfare instead of looking for a job and making sure they remain childless throughout their adolescence. The middle class and the rich feel they are working so hard and the poor are not. These poor people are lumped together with the criminals and accused of making the streets unsafe. The poor have become an excuse or scapegoat for the problems in society. Instead of admitting the decline in morality, the poor are accused of being the only ones with unmarried lovers. Once they get their life in order then they can receive welfare. They are being forced to live up to moral expectations that the working class and the rich speak but do not practice (Hans, 2007, pg 506).

Clearly class bigotry needs to be addressed. The poor have moral failings that are highly noticeable than the middle class but it does not mean it is at a higher proportion. The rich and middle class have access to counseling facilities to tell them their moral failings is as a result of prior abuse or disease.

The poor do not want to marry the fathers of their babies as they are jobless. There is actually scarcity of work; it is not true that the poor do not want to work. The government should address poverty through actively engaging in job creation initiatives and ensuring the actual crime of the poor does not fall below a certain percentage.

The War Against the Poor: Critical Review

The author’s views on class discrimination agree with my views. He concurs that judging the poor harshly for their moral failings and the ability to secure a job is wrong. The middle class and the rich also have moral failings and the middle class has also been experiencing unemployment as jobs are scarce.

Crime and mental illnesses should be viewed as some of the effects of poverty. It is not that the poor and mostly the Blacks have higher criminal tendencies. The middle class and the rich to stop discriminating against the poor and having someone to blame.

The author has also highlighted other concerns that I agree with. Hans says that the government, politicians and public are making life tougher for the poor. I agree with Hans that the focus should be on creation of jobs for the poor. If the country does not stop attacking the poor, the morale, quality of life and economic competitiveness will only go down.

Discrimination in Middle of the Class

The article Middle of the class published in the Economists is an argumentative piece of writing that questions the sustainability of the American Dream. America has always been defined as a country where anyone can become rich or wealthy if they just work hard. Shows like American Idol prove this.

The country has had presidents from humble backgrounds like Benjamin Franklin who was the 15 th child of a candle maker. However the equality of opportunity in America for all its citizens is rapidly diminishing.

The author gives the statistical figures on how the rich have become richer while the poor have become even poorer widening the income gap even more. Secondly social mobility has gone down. A lower and lower percentage of people are able to change the social class they are in through increase in earnings over a period of ten years.

There have also been changes in the economy with a shift towards technical skills requiring workers who have a university degree. This has caused a high increase of the income gap between college and high school graduates. It has become hard to climb the corporate ladder or change jobs if one does not have a university degree. The author suggests that the American society is becoming an educational stratified society

in other words a meritocracy. The rise in university education is also providing a hurdle for middle class families to attend elite universities. The representation of the rich in these elite universities has increased more than the representation of the poor. The mean income of the families that have enrolled their children in Harvard is $150,000(The Economist, 2007, pg 528).

During the period 2001-2004, States found themselves facing a budget squeeze. They responded by increasing the fees of state colleges where the middle class take their children to learn. This proves that the American system is enforcing more income inequalities through educational differences. The rich children are more likely to get a degree than a child from the bottom quarter income level.

There is also a worrying trend in the society that further aggravates class and educational stratification. The chances of an individual getting access to a good education, a good job and good prospects in life is determined by the family the person is born into.

College graduates tend to marry college graduates. Therefore in the graduates home the returns of the degree is double and their children benefit even more with opportunities to attend better schools.

There is therefore great trouble in being poor. If in the American society to be socially mobile you must have a great education, a job and married with children then the rich start off with higher advantages.

There needs to be policy changes where the method by which schools are financed is changed and giving more federal help to poorer colleges. This will only happen when the American politicians and the public recognize there is a problem.

Middle of the Class: Critical Review

The author, like Hans concurs with my argument that the poor are being judged too harshly in society. The reason the poor are not able to support themselves is not that they are lazy or lack ambition.

Rather there is a limitation on the equality of opportunity when it comes to the middle class and the poor in the corporate world. The country is being affected by globalization and technology changes; therefore the requirement of a degree is becoming mandatory.

If what it takes to succeed in the American corporate society is the attainment of a degree then the government should ensure that children from all social backgrounds have access to this type of education. Making education costs high does not help the poor and middle class at all.

It only goes to aggravate the existent inequalities between the rich and the poor. As the author has given statistics, in the last few years the rich have been becoming richer and the poor becoming poorer. The government needs to step in and address the situation.

Social Inequality in When Shelter Feels Like a Prison

The two articles narrated on the stereotypes held by society towards the poor while the article in the Economist discusses the widening gap between the rich and the poor. Both papers focus on the poor. The third article written by Charmion Brown tells of the author’s experiences growing up in a homeless shelter. The real life story further reinforces my argument on the distancing of the poor by society.

In light of her first hand experiences in the place she feels she can only compare it a prison. First of all, the place is cramped with four bunk beds fitted in each tiny room (Browne, 2007, pg 531).

There is absolutely no privacy. One has to take care of their things or they will be stolen. There is a queue for food for the homeless. The author learnt that if you do not make the line two hours before the kitchen is open, one would miss food. There are no curtains in the bathrooms yet the facility is being shared by more than one hundred people. The author felt like the place was a prison.

When Shelter Feels Like a Prison: Critical Review

The author’s experiences in the shelter confirm my views on the abandonment of the poor and homeless in the shelters. The author narrates how the social workers are rare and have no time for them. It is a prison. The government and public needs to stop abandoning the shelters. The living conditions needs to be improved. In my argument I had put forward the assumptions society has concerning the poor people.

They are not successful because they are lazy. The author cautions society and informs them that there were people from broken homes in the shelter due to drug abuse, AIDS and early pregnancy and not because they are lazy. The poor also lack knowledge on how to improve their lives.

The three articles have gone further to reinforce my argument on the existence of negative attitudes and stereotypes for the poor in society. Hans goes further to explain that it is because the poor have become a scapegoat to make other members in the society better. In my argument I had put forward the way society views the world in black and white. The hardworking succeed the poor are the lazy ones.

The article in the economist supports my argument and goes ahead to tell society that actually there is a limitation on equality of opportunity in the country. One may desire a job but he cannot get that job. In my argument I also said that the society distances itself from the poor. The article, When Shelter feels like a Prison clearly shows the abandonment of the poor by society.

Browne, Charmion. “When Shelter Feels Like a Prison” Writing in the Disciplines: A Reader for Writers . Ed. Mary Kennedy. 6 th Ed. New Jersey: Prentice Hall. 2007. Print.

Hans, Herbert. “The war against the poor instead of programs to end poverty” Writing in the Disciplines: A Reader for Writers . Ed. Mary Kennedy. 6 th Ed. New Jersey: Prentice Hall. 2007. Print.

The Economist. “The Middle Class” Writing in the Disciplines: A Reader for Writers . Ed. Mary Kennedy. 6 th Ed. New Jersey: Prentice Hall. 2007. Print.

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Bibliography

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• Active versus Lazy People
• Chinese Civilization During the Han Dynasty
• Comparison Between Confucius and Han Feizi
• Are Word Processing Programs Making Students Lazy?
• Asian Studies: The Han as a Confucian State
• Break up of a Relationship
• Causes of Divorce in America
• Five Viewpoints on Human Nature
• Pre Marriage Counseling: One Year Before Getting Married
• Gender and Media – Relation to Women